[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-youngfish42--Awesome-FL":3,"tool-youngfish42--Awesome-FL":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",152630,2,"2026-04-12T23:33:54",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":79,"owner_twitter":80,"owner_website":81,"owner_url":82,"languages":83,"stars":92,"forks":93,"last_commit_at":94,"license":95,"difficulty_score":96,"env_os":97,"env_gpu":98,"env_ram":98,"env_deps":99,"category_tags":102,"github_topics":104,"view_count":125,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":126,"updated_at":127,"faqs":128,"releases":149},2052,"youngfish42\u002FAwesome-FL","Awesome-FL","Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)","Awesome-FL 是一个专注于联邦学习（Federated Learning）领域的开源资源聚合平台，旨在为学术界和工业界提供全面、及时的学术信息。它系统地整理了该领域的高质量论文、主流开发框架、基准数据集、教程课程以及相关的研讨会资讯，并按人工智能、机器学习、安全隐私、计算机视觉、自然语言处理等细分方向进行了详细分类。\n\n在联邦学习研究快速迭代的背景下，研究人员往往面临文献分散、资源查找困难的问题。Awesome-FL 通过结构化的知识库，帮助用户高效定位顶会顶刊论文与实用代码工具，极大地降低了入门门槛和调研成本。其独特亮点在于不仅涵盖通用联邦学习资源，还深入梳理了图数据、表格数据等特定场景下的前沿进展，并曾利用自动化项目追踪论文更新动态。\n\n该资源主要适合从事联邦学习算法研究的高校科研人员、研究生，以及需要落地隐私计算技术的开发者使用。无论是希望快速了解领域全貌的初学者，还是寻求最新技术突破的资深专家，都能从中获得宝贵参考。需要注意的是，随着核心维护者完成博士学业并调整研究重心，目前的更新频率已调整为月度或季度，部分深度维护内容有所精简，但其积累的海量历史资源依然具有极高的查阅","Awesome-FL 是一个专注于联邦学习（Federated Learning）领域的开源资源聚合平台，旨在为学术界和工业界提供全面、及时的学术信息。它系统地整理了该领域的高质量论文、主流开发框架、基准数据集、教程课程以及相关的研讨会资讯，并按人工智能、机器学习、安全隐私、计算机视觉、自然语言处理等细分方向进行了详细分类。\n\n在联邦学习研究快速迭代的背景下，研究人员往往面临文献分散、资源查找困难的问题。Awesome-FL 通过结构化的知识库，帮助用户高效定位顶会顶刊论文与实用代码工具，极大地降低了入门门槛和调研成本。其独特亮点在于不仅涵盖通用联邦学习资源，还深入梳理了图数据、表格数据等特定场景下的前沿进展，并曾利用自动化项目追踪论文更新动态。\n\n该资源主要适合从事联邦学习算法研究的高校科研人员、研究生，以及需要落地隐私计算技术的开发者使用。无论是希望快速了解领域全貌的初学者，还是寻求最新技术突破的资深专家，都能从中获得宝贵参考。需要注意的是，随着核心维护者完成博士学业并调整研究重心，目前的更新频率已调整为月度或季度，部分深度维护内容有所精简，但其积累的海量历史资源依然具有极高的查阅价值。","# Federated Learning Resources\n\n[![Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fyoungfish42\u002FAwesome-FL.svg?color=orange)](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fstargazers) [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge-flat.svg)](https:\u002F\u002Fawesome.re) [![License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fyoungfish42\u002FAwesome-FL.svg?color=green)](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002Fimage-registration-resources\u002Fblob\u002Fmaster\u002FLICENSE) ![](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002Fyoungfish42\u002FAwesome-FL) \n\n---\n\n**Table of Contents**\n\n- [Papers](#papers)\n  - [FL in top-tier journal](#fl-in-top-tier-journal)\n  - FL in top-tier conference and journal by category\n    - [AI](#fl-in-top-ai-conference-and-journal) [ML](#fl-in-top-ml-conference-and-journal) [DM](#fl-in-top-dm-conference-and-journal) [Secure](#fl-in-top-secure-conference-and-journal) [CV](#fl-in-top-cv-conference-and-journal) [NLP](#fl-in-top-nlp-conference-and-journal) [IR](#fl-in-top-ir-conference-and-journal) [DB](#fl-in-top-db-conference-and-journal) [Network](#fl-in-top-network-conference-and-journal) [System](#fl-in-top-system-conference-and-journal) [Others](#fl-in-top-conference-and-journal-other-fields)\n  - [FL on Graph Data and Graph Neural Networks](#fl-on-graph-data-and-graph-neural-networks) [![dblp](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A%2F%2Fdblp.org%2Fsearch%2Fpubl%2Fapi%3Fq%3DFederated%2520graph%257Csubgraph%257Cgnn%26format%3Djson%26h%3D1000)](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=Federated%20graph%7Csubgraph%7Cgnn) \n  - [FL on Tabular Data](#fl-on-tabular-data) [![dblp](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A\u002F\u002Fdblp.org\u002Fsearch\u002Fpubl\u002Fapi%3Fq%3Dfederate%2520tree%257Cboost%257Cbagging%257Cgbdt%257Ctabular%257Cforest%257CXGBoost%26format%3Djson%26h%3D1000)](https:\u002F\u002Fdblp.org\u002Fsearch?q=federate%20tree%7Cboost%7Cbagging%7Cgbdt%7Ctabular%7Cforest%7CXGBoost)\n- [Framework](#framework)\n- [Datasets](#datasets)\n- [Surveys](#surveys)\n- [Tutorials and Courses](#tutorials-and-courses)\n- Key Conferences\u002FWorkshops\u002FJournals\n  - [Workshops](#workshops) [Special Issues](#journal-special-issues) [Special Tracks](#conference-special-tracks)\n- [Update log](#update-log)\n- [Acknowledgments](#acknowledgments)\n- [Citation](#citation)\n\n\n\nWe use another project to automatically track updates to FL papers, click on [FL-paper-update-tracker](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FFL-paper-update-tracker) if you need it.\n\nPlease note that if this page does not display the full content, **please visit [the official homepage](https:\u002F\u002Fyoungfish42.github.io\u002FAwesome-FL) for full information.**\n\n**More items will be added to the repository**. Please feel free to suggest other key resources by opening an [issue](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues) report, submitting a pull request, or dropping me an email @ ([im.young@foxmail.com](mailto:im.young@foxmail.com)). If you want to communicate with more friends in the field of federated learning, please join the QQ group [联邦学习交流群], the group number is 833638275. Enjoy reading!\n\n\n\n**Repository Update Notice** \n\n> 2024\u002F09\u002F30\n>\n> \n>\n> Dear Users, We would like to inform you of a few changes that will affect this open source repository. The owner and principal contributor [@youngfish42](https:\u002F\u002Fgithub.com\u002Fyoungfish42) has successfully completed his doctoral studies 🎓 as of September 30, 2024, and has since shifted his research focus. This change in circumstances will impact the frequency and extent of updates to the repository's paper list. \n>\n> Instead of the previous regular updates, we anticipate that the paper list will now be updated on a monthly or quarterly basis. Furthermore, the depth of these updates will be reduced. For instance, updates related to the author's institution and open source code will no longer be actively maintained. \n>\n> We understand that this might affect the value you derive from this repository. Therefore, we humbly invite more contributors to participate in updating the content. This collaborative effort will ensure that the repository remains a valuable resource for everyone. \n>\n> We appreciate your understanding and look forward to your continued support and contributions. \n>\n> \n>\n> Best Regards, \n>\n> 白小鱼 (youngfish)\n>\n\n\n\n\n# papers\n\n**categories**\n\n- Artificial Intelligence (IJCAI, AAAI, AISTATS, ALT, AI)\n\n- Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI, Machine Learning, JMLR, TPAMI)\n\n- Data Mining (KDD, WSDM)\n\n- Secure (S&P, CCS, USENIX Security, NDSS)\n\n- Computer Vision (ICCV, CVPR, ECCV, MM, IJCV)\n\n- Natural Language Processing (ACL, EMNLP, NAACL, COLING)\n\n- Information Retrieval (SIGIR)\n\n- Database (SIGMOD, ICDE, VLDB)\n\n- Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)\n\n- System (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC) \n\n- Others (ICSE, FOCS, STOC)\n\n\n\n\n\u003Cdetails open>\n\u003Csummary> Events \u003C\u002Fsummary>\n\n| Venue                                                        | 2024-2020                                                    | before 2020                                                  |\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |\n| [IJCAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AIJCAI%3A) | [25](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F), [24](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F), [23](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F), [22](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F), [21](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F), [20](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F) | [19](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F)                |\n| [AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAAAI%3A) | [25](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2025.html), [24](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2024.html), [23](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2023), [22](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-22\u002Fwp-content\u002Fuploads\u002F2021\u002F12\u002FAAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [21](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-21\u002Fwp-content\u002Fuploads\u002F2020\u002F12\u002FAAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [20](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FAAAI-20-Accepted-Paper-List.pdf) | -                                                            |\n| [AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAISTATS%3A) | [25](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002F), [24](http:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002F), [23](http:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002F), [22](http:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002F), [21](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002F), [20](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002F) | -                                                            |\n| [ALT](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Falt%3A) | 22                                                           | -                                                            |\n| [AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fai%3A) (J) | 25, 23                                                       | -                                                            |\n| [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANeurIPS%3A) | [24](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2024\u002FConference#tab-accept-oral), [23](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2023\u002FConference#tab-accept-oral), [22](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2022), [21](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021), [20](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020) | [18](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018), [17](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F17) |\n| [ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICML%3A) | [25](https:\u002F\u002Ficml.cc\u002FConferences\u002F2025\u002FSchedule?type=Poster), [24](https:\u002F\u002Ficml.cc\u002FConferences\u002F2024\u002FSchedule?type=Poster), [23](https:\u002F\u002Ficml.cc\u002FConferences\u002F2023\u002FSchedule?type=Poster), [22](https:\u002F\u002Ficml.cc\u002FConferences\u002F2022\u002FSchedule?type=Poster), [21](https:\u002F\u002Ficml.cc\u002FConferences\u002F2021\u002FSchedule?type=Poster), [20](https:\u002F\u002Ficml.cc\u002FConferences\u002F2020\u002FSchedule?type=Poster) | [19](https:\u002F\u002Ficml.cc\u002FConferences\u002F2019\u002FSchedule?type=Poster)  |\n| [ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICLR%3A) | [25](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2025), [24](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2024\u002FConference), [23](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2023\u002FConference), [22](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2022\u002FConference), [21](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2021\u002FConference), [20](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2020\u002FConference) | -                                                            |\n| [COLT](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ACOLT%3A) | [23](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002F)                    | -                                                            |\n| [UAI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AUAI%3A)  | [25](https:\u002F\u002Fwww.auai.org\u002Fuai2025\u002Faccepted_papers), [24](https:\u002F\u002Fwww.auai.org\u002Fuai2024\u002Faccepted_papers), [23](https:\u002F\u002Fwww.auai.org\u002Fuai2023\u002Faccepted_papers), [22](https:\u002F\u002Fwww.auai.org\u002Fuai2022\u002Faccepted_papers), [21](https:\u002F\u002Fwww.auai.org\u002Fuai2021\u002Faccepted_papers) | -                                                            |\n| [Machine Learning](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fml%3A) (J) | 25, 24, 23, 22                                               | -                                                            |\n| [JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fjmlr%3A) (J) | 24, 23, 22                                                   | -                                                            |\n| [TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpami%3A) (J) | 25, 24, 23, 22                                               | -                                                            |\n| [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AKDD%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3690624), [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3637528), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3580305), [22](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperRT.html), [21](https:\u002F\u002Fkdd.org\u002Fkdd2021\u002Faccepted-papers\u002Findex), [20](https:\u002F\u002Fwww.kdd.org\u002Fkdd2020\u002Faccepted-papers) |                                                              |\n| [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWSDM%3A) | [25](https:\u002F\u002Fwww.wsdm-conference.org\u002F2025\u002Faccepted-papers\u002F), [24](https:\u002F\u002Fwww.wsdm-conference.org\u002F2024\u002Faccepted-papers\u002F), [23](https:\u002F\u002Fwww.wsdm-conference.org\u002F2023\u002Fprogram\u002Faccepted-papers), [22](https:\u002F\u002Fwww.wsdm-conference.org\u002F2022\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww.wsdm-conference.org\u002F2021\u002Faccepted-papers.php) | [19](https:\u002F\u002Fwww.wsdm-conference.org\u002F2019\u002Faccepted-papers.php) |\n| [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsp%3A) | [25](https:\u002F\u002Fsp2025.ieee-security.org\u002Fprogram-papers.html), [24](https:\u002F\u002Fsp2024.ieee-security.org\u002Fprogram-papers.html), [23](https:\u002F\u002Fsp2023.ieee-security.org\u002Fprogram-papers.html), [22](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2022\u002Fprogram-papers.html) | [19](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002Fprogram-papers.html) |\n| [CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACCS%3A) | [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3658644), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3576915), [22](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2022\u002Fprogram\u002Faccepted-papers.html), [21](https:\u002F\u002Fsigsac.org\u002Fccs\u002FCCS2021\u002Faccepted-papers.html), [19](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2019\u002Findex.php\u002Fprogram\u002Faccepted-papers\u002F) | [17](https:\u002F\u002Facmccs.github.io\u002Fpapers\u002F)                       |\n| [USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fuss%3A) | [23](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Ftechnical-sessions), [22](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Ftechnical-sessions), [20](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Ftechnical-sessions) | -                                                            |\n| [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANDSS%3A) | [25](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2025\u002Faccepted-papers\u002F), [24](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2024\u002Faccepted-papers\u002F), [23](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2023\u002Faccepted-papers\u002F), [22](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2022\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2021\u002Faccepted-papers\u002F) | -                                                            |\n| [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACVPR%3A) | [25](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2025?day=all), [24](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2024?day=all), [23](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2023?day=all), [22](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2022), [21](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2021?day=all) | -                                                            |\n| [ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICCV%3A) | [23](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2023?day=all),[21](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2021?day=all) | -                                                            |\n| [ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AECCV%3A) | [24](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [22](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [20](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php) | -                                                            |\n| [MM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fmm%3A) | [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3664647), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3581783), [22](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fmm\u002Fmm2022.html), [21](https:\u002F\u002F2021.acmmm.org\u002Fmain-track-list), [20](https:\u002F\u002F2020.acmmm.org\u002Fmain-track-list.html) | -                                                            |\n| [IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fijcv%3A) (J) | 25, 24                                                       | -                                                            |\n| [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AACL%3A) | [25](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2025\u002F), [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2024\u002F), [23](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2023\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2021\u002F) | [19](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2019\u002F)              |\n| [NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANAACL-HLT%3A) | [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2024\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2021\u002F) | -                                                            |\n| [EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AEMNLP%3A) | [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2024\u002F), [23](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2023\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2021\u002F), [20](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2020\u002F) | -                                                            |\n| [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACOLING%3A) | [25](https:\u002F\u002Faclanthology.org\u002Fvolumes\u002F2025.coling-main\u002F), [20](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fcoling-2020\u002F) | -                                                            |\n| [SIGIR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGIR%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3726302), [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3626772), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3539618), [22](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3477495), [21](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3404835), [20](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3397271) | -                                                            |\n| [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsigmod%3A) | [22](https:\u002F\u002F2022.sigmod.org\u002Fsigmod_research_list.shtml), [21](https:\u002F\u002F2021.sigmod.org\u002Fsigmod_research_list.shtml) | -                                                            |\n| [ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICDE%3A) | [25](https:\u002F\u002Fieee-icde.org\u002F2025\u002Fresearch-papers\u002F), [24](https:\u002F\u002Ficde2024.github.io\u002F), [23](https:\u002F\u002Ficde2023.ics.uci.edu\u002Fpapers-research-track\u002F), [22](https:\u002F\u002Ficde2022.ieeecomputer.my\u002Faccepted-research-track\u002F), [21](https:\u002F\u002Fieeexplore.ieee.org\u002Fxpl\u002Fconhome\u002F9458599\u002Fproceeding) | -                                                            |\n| [VLDB](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | [25](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F18), [24](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [23](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [22](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol16-volume-info\u002F), [21](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol15-volume-info\u002F), [21](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002F), [20](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13-volume-info\u002F) | -                                                            |\n| [SIGCOMM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGCOMM%3A) | 25                                                           | -                                                            |\n| [INFOCOM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AINFOCOM%3A) | [25](https:\u002F\u002Finfocom2025.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [24](https:\u002F\u002Finfocom2024.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [23](https:\u002F\u002Finfocom2023.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [22](https:\u002F\u002Finfocom2022.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [21](https:\u002F\u002Finfocom2021.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), [20](https:\u002F\u002Finfocom2020.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html) | [19](https:\u002F\u002Finfocom2019.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), 18 |\n| [MobiCom](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AMobiCom%3A) | [24](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2024\u002Faccepted.html), [23](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2023\u002Faccepted.html), [22](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2022\u002Faccepted.html), [21](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2021\u002Faccepted.html), [20](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2020\u002Faccepted.php) |                                                              |\n| [NSDI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANSDI%3A) | [25](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Ftechnical-sessions), 23([1](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Fspring-accepted-papers), [2](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Ffall-accepted-papers)) | -                                                            |\n| [WWW](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWWW%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3696410), [24](https:\u002F\u002Fwww2024.thewebconf.org\u002Faccepted\u002Fresearch-tracks\u002F), [23](https:\u002F\u002Fwww2023.thewebconf.org\u002Fprogram\u002Faccepted-papers\u002F), [22](https:\u002F\u002Fwww2022.thewebconf.org\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww2021.thewebconf.org\u002Fprogram\u002Fpapers-program\u002Flinks\u002Findex.html) |                                                              |\n| [OSDI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AOSDI%3A) | 21                                                           | -                                                            |\n| [SOSP](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ASOSP%3A) | 21                                                           | -                                                            |\n| [ISCA](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AISCA%3A) | [24](https:\u002F\u002Fwww.iscaconf.org\u002Fisca2024\u002Fprogram\u002F)             | -                                                            |\n| [MLSys](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AMLSys%3A) | [24](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2024), [23](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2023), [22](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2022), [20](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2020) | [19](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2019)   |\n| [EuroSys](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Feurosys%3A) | [25](https:\u002F\u002F2025.eurosys.org\u002Faccepted-papers.html), [24](https:\u002F\u002F2024.eurosys.org\u002Faccepted-papers.html), [23](https:\u002F\u002F2023.eurosys.org\u002Faccepted-papers.html), 22, 21, 20 |                                                              |\n| [TPDS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Ftpds%3A) (J) | 25, 24, 23, 22, 21, 20                                       | -                                                            |\n| [DAC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ADAC%3A) | 25, 24, 22, 21                                               | -                                                            |\n| [TOCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftocs%3A) | -                                                            | -                                                            |\n| [TOS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftos%3A) | -                                                            | -                                                            |\n| [TCAD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftcad%3A) | 25, 24, 23, 22, 21                                           | -                                                            |\n| [TC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftc%3A) | 25, 24, 23, 22, 21                                           | -                                                            |\n| [ICSE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Ficse%3A) | [25](https:\u002F\u002Fconf.researchr.org\u002Ftrack\u002Ficse-2025\u002Ficse-2025-research-track), [23](https:\u002F\u002Fconf.researchr.org\u002Ftrack\u002Ficse-2023\u002Ficse-2023-technical-track?#event-overview), 21 | -                                                            |\n| [FOCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ffocs%3A) | -                                                            | -                                                            |\n| [STOC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Fstoc%3A) | -                                                            | -                                                            |\n\n\u003C\u002Fdetails>\n\n\n\n\n**keywords**\n\nStatistics: :fire: code is available & stars >= 100 | :star: citation >= 50 | :mortar_board: Top-tier venue \n\n**`kg.`**: Knowledge Graph |   **`data.`**: dataset  |   **`surv.`**: survey\n\n\n\n\n## fl in top-tier journal\n\nPapers of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to [WOS](https:\u002F\u002Fwww.webofscience.com\u002Fwos\u002Fwoscc\u002Fsummary\u002Fed3f4552-5450-4de7-bf2c-55d01e20d5de-4301299e\u002Frelevance\u002F1) search engine.\n\n\u003Cdetails open>\n\u003Csummary>fl in top-tier journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-tier-journal -->\n\n|Title                                                           |    Affiliation    |    Venue                    |    Year    |    Materials|\n| ------------------------------------------------------------ | ----------- | --------------------- | ---- | ------------------------------------------------------------ |\n| Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption |  | Nat. Mach. Intell. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-01107-6)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2408.06197)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsiyang-jiang\u002FLancelot)] |\n| Incentivizing inclusive contributions in model sharing markets |  | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-62959-5)] [[CODE](https:\u002F\u002Fgithub.com\u002F19dx\u002FiPFL)] |\n| FedECA: federated external control arms for causal inference with time-to-event data in distributed settings |  | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-62525-z)] [[CODE](https:\u002F\u002Fgithub.com\u002Fowkin\u002Ffedeca)] |\n| Privacy-preserving multicenter differential protein abundance analysis with FedProt |  | Nat. Comput. Sci. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-025-00832-7)] [[CODE](https:\u002F\u002Fgithub.com\u002FFreddsle\u002FFedProt)] |\n| Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge |  | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-60466-1)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmlcommons\u002Fmedperf\u002Ftree\u002Ffets-challenge)] |\n| A fully open AI foundation model applied to chest radiography |  | Nature | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-025-09079-8)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjlianglab\u002FArk)] |\n| Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator |  | Nat. Electron. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41928-025-01390-6)] |\n| A framework reforming personalized Internet of Things by federated meta-learning | SYSU | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-59217-z)] [[CODE](https:\u002F\u002Fgithub.com\u002FIntelligentSystemsLab\u002Fgeneric_and_open_learning_federator\u002F)] |\n| Achieving flexible fairness metrics in federated medical imaging | CUHK | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-58549-0)] [[CODE](https:\u002F\u002Fzenodo.org\u002Frecords\u002F15203267)] |\n| Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare | HIT | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-58055-3)] [[CODE](https:\u002F\u002Fgithub.com\u002Fparidis-11\u002FDynamicFL)] |\n| Data-driven federated learning in drug discovery with knowledge distillation | Lhasa Limited | Nat. Mach. Intell. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-00991-2)] [[CODE](https:\u002F\u002Fgithub.com\u002FLhasaLimited\u002FFLuID_POC)] |\n| Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals | Yale University; UCSD | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-56510-9)] |\n| Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models | PKU | Nat. Commun. | 2025 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-56412-w)] [[新闻](https:\u002F\u002Fic.pku.edu.cn\u002Fkxyj\u002Fkycg1\u002Fd2c084006150492c93ae3e6b0cb1d7df.htm)] |\n| MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing | USTB; NTU | Nat. Commun. | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-53431-x)] [[CODE](https:\u002F\u002Fgithub.com\u002FSICC-Group\u002FMatSwarm)] |\n| Introducing edge intelligence to smart meters via federated split learning | HKU | Nat. Commun. | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-53352-9)] [[新闻](https:\u002F\u002Fwww.ces.org.cn\u002Fhtml\u002Freport\u002F24110829-1.htm)] |\n| An international study presenting a federated learning AI platform for pediatric brain tumors | Stanford University | Nat. Commun. | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-51172-5)] [[CODE](https:\u002F\u002Fgithub.com\u002Fedhlee\u002FFLPedBrain)] |\n| PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data | KAUST | Science Advances | 2024 | [[PUB](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fsciadv.adh8601)] [[CODE](https:\u002F\u002Fgithub.com\u002FJoshuaChou2018\u002FPPML-Omics)] |\n| Federated learning is not a cure-all for data ethics | TUM; UvA | Nat. Mach. Intell.(Comment) | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-024-00813-x)] |\n| Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence | Jiangmen Central Hospital; Guilin University of Aerospace Technology; Guilin University of Electronic Technology; | Nat. Commun. | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-44946-4)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaofengguat\u002FRFLM-project\u002F)] |\n| Selective knowledge sharing for privacy-preserving federated distillation without a good teacher | HKUST | Nat. Commun. | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-44383-9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01731)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshaojiawei07\u002FSelective-FD)] |\n| A federated learning system for precision oncology in Europe: DigiONE | IQVIA Cancer Research BV | Nat. Med. (Comment) | 2024 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-023-02715-8)] |\n| Multi-client distributed blind quantum computation with the Qline architecture | Sapienza Università di Roma | Nat. Commun. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43617-0)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05195)] |\n| Device-independent quantum randomness–enhanced zero-knowledge proof | USTC | PNAS | 2023 | [[PUB](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002F10.1073\u002Fpnas.2205463120)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.06717)] [[新闻](https:\u002F\u002Fwww.nsfc.gov.cn\u002Fpublish\u002Fportal0\u002Ftab448\u002Finfo90817.htm)] |\n| Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Tsinghua University | Nat. Commun. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43883-y)] |\n| Advocating for neurodata privacy and neurotechnology regulation | Columbia University | Nat. Protoc. (Perspective) | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41596-023-00873-0)] |\n| Federated benchmarking of medical artificial intelligence with MedPerf | IHU Strasbourg; University of Strasbourg; Dana-Farber Cancer Institute; Weill Cornell Medicine; Harvard T.H. Chan School of Public Health; MIT; Intel | Nat. Mach. Intell. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-023-00652-2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.01406)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmlcommons\u002FMedPerf)] |\n| Algorithmic fairness in artificial intelligence for medicine and healthcare | Harvard Medical School; Broad Institute of Harvard and Massachusetts Institute of Technology; Dana-Farber Cancer Institute | Nat. Biomed. Eng. (Perspective) | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-023-01056-8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00603)] |\n| Differentially private knowledge transfer for federated learning | THU | Nat. Commun. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-38794-x)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftaoqi98\u002FPrivateKT)] |\n| Decentralized federated learning through proxy model sharing | Layer 6 AI; University of Waterloo; Vector Institute | Nat. Commun. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-38569-4)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.11343)] [[CODE](https:\u002F\u002Fgithub.com\u002Flayer6ai-labs\u002FProxyFL)] |\n| Federated machine learning in data-protection-compliant research | University of Hamburg | Nat. Mach. Intell.(Comment) | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00601-5)] |\n| Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer | Owkin | Nat. Med. | 2023 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-022-02155-w)] [[CODE](https:\u002F\u002Fgithub.com\u002FSubstra\u002Fsubstra)] |\n| Federated learning enables big data for rare cancer boundary detection | University of Pennsylvania | Nat. Commun. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-33407-5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10836)] [[CODE](https:\u002F\u002Fgithub.com\u002FFETS-AI\u002FFront-End)] |\n| Federated learning and Indigenous genomic data sovereignty | Hugging Face | Nat. Mach. Intell. (Comment) | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00551-y)] |\n| Federated disentangled representation learning for unsupervised brain anomaly detection | TUM | Nat. Mach. Intell. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00515-2)] [[PDF](https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-722389\u002Fv1)] [[CODE](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.6604161)] |\n| Shifting machine learning for healthcare from development to deployment and from models to data | Stanford University; Greenstone Biosciences | Nat. Biomed. Eng. (Review Article) | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-022-00898-y)] |\n| A federated graph neural network framework for privacy-preserving personalization | THU | Nat. Commun. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30714-9)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwuch15\u002FFedPerGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F487383715)] |\n| Communication-efficient federated learning via knowledge distillation | THU | Nat. Commun. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-29763-x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.13323)] [[CODE](https:\u002F\u002Fzenodo.org\u002Frecord\u002F6383473)] |\n| Lead federated neuromorphic learning for wireless edge artificial intelligence | XMU; NTU | Nat. Commun. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-32020-w)] [[CODE](https:\u002F\u002Fgithub.com\u002FGOGODD\u002FFL-EDGE-COMPUTING\u002Freleases\u002Ftag\u002Ffederated_learning)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F549087420)] |\n| A novel decentralized federated learning approach to train on globally  distributed, poor quality, and protected private medical data | University of Wollongong | Sci. Rep. | 2022 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-022-12833-x)] |\n| Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | HUST | Nat. Mach. Intell. | 2021 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00421-z)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09461)] [[CODE](https:\u002F\u002Fgithub.com\u002FHUST-EIC-AI-LAB\u002FUCADI)] |\n| Federated learning for predicting clinical outcomes in patients with COVID-19 | MGH radiology and Harvard Medical School | Nat. Med. | 2021 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3)] [[CODE](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3#code-availability)] |\n| Adversarial interference and its mitigations in privacy-preserving collaborative machine learning | Imperial College London; TUM;  OpenMined | Nat. Mach. Intell.(Perspective) | 2021 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00390-3)] |\n| Swarm Learning for decentralized and confidential clinical machine learning :star: | DZNE; University of Bonn; | Nature :mortar_board: | 2021 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-021-03583-3)] [[CODE](https:\u002F\u002Fgithub.com\u002FHewlettPackard\u002Fswarm-learning)] [[SOFTWARE](https:\u002F\u002Fmyenterpriselicense.hpe.com)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F379434722)] |\n| End-to-end privacy preserving deep learning on multi-institutional medical imaging | TUM; Imperial College London; OpenMined | Nat. Mach. Intell. | 2021 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00337-8)] [[CODE](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.4545599)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F484801505)] |\n| Communication-efficient federated learning | CUHK; Princeton University | PANS. | 2021 | [[PUB](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002Ffull\u002F10.1073\u002Fpnas.2024789118)] [[CODE](https:\u002F\u002Fcode.ihub.org.cn\u002Fprojects\u002F4394\u002Frepository\u002Frevisions\u002Fmaster\u002Fshow\u002FPNAS)] |\n| Breaking medical data sharing boundaries by using synthesized radiographs | RWTH Aachen University | Science. Advances. | 2020 | [[PUB](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fsciadv.abb7973)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpeterhan91\u002FThorax_GAN)] |\n| Secure, privacy-preserving and federated machine learning in medical imaging :star: | TUM; Imperial College London; OpenMined | Nat. Mach. Intell.(Perspective) | 2020 | [[PUB](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-020-0186-1)] |\n\n\u003C!-- END:fl-in-top-tier-journal -->\n\n\u003C\u002Fdetails>\n\n\n\n## fl in top ai conference and journal\n\nFederated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including [IJCAI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fijcai\u002Findex.html)(International Joint Conference on Artificial Intelligence), [AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Faaai\u002Findex.html)(AAAI Conference on Artificial Intelligence), [AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Faistats\u002Findex.html)(Artificial Intelligence and Statistics), [ALT](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Falt\u002Findex.html)(International Conference on Algorithmic Learning Theory), [AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fai\u002Findex.html)(Artificial Intelligence).\n\n- [IJCAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AIJCAI%3A) [2025](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F), [2024](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F), [2023](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F), [2022](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F), [2021](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F), [2020](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F), [2019](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F)\n- [AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAAAI%3A) [2025](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2025.html), [2024](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2024.html), [2023](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2023), [2022](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-22\u002Fwp-content\u002Fuploads\u002F2021\u002F12\u002FAAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [2021](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-21\u002Fwp-content\u002Fuploads\u002F2020\u002F12\u002FAAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [2020](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FAAAI-20-Accepted-Paper-List.pdf)\n- [AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAISTATS%3A) [2025](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002F), [2024](http:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002F), [2023](http:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002F), [2022](http:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002F), [2021](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002F), [2020](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002F)\n- [ALT](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Falt%3A) 2022\n- [AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fai%3A) 2025, 2023\n\n\u003Cdetails open>\n\u003Csummary>fl in top ai conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-ai-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                     |    Venue      |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | ---- | ------------------------------------------------------------ |\n| Exploiting Label Skewness for Spiking Neural Networks in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F767)] |\n| FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F379)] |\n| Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F610)] |\n| Pixel-wise Divide and Conquer for Federated Vessel Segmentation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F540)] |\n| Universal Backdoor Defense via Label Consistency in Vertical Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F528)] |\n| Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F536)] |\n| Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F541)] [[COCE](https:\u002F\u002Fgithub.com\u002FlancasterJie\u002FFLAYER)] |\n| FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F559)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdingbinb\u002FFedDLAD)] |\n| Federated Multi-view Graph Clustering with Incomplete Attribute Imputation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F570)] |\n| ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F585)] |\n| Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F590)] |\n| FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F597)] |\n| FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F602)] |\n| FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F333)] |\n| FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F612)] |\n| Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F146)] |\n| Distilling A Universal Expert from Clustered Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F620)] |\n| CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F621)] |\n| FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F628)] |\n| Hypernetwork Aggregation for Decentralized Personalized Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F161)] |\n| Federated Domain Generalization with Decision Insight Matrix |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F633)] |\n| Generic Adversarial Attack Framework Against Vertical Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F646)] |\n| One-shot Federated Learning Methods: A Practical Guide |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1174)] |\n| Federated Learning at the Forefront of Fairness: A Multifaceted Perspective |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1177)] |\n| Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F670)] |\n| Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F677)] |\n| FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F692)] [[CODE](https:\u002F\u002Fgithub.com\u002FYuxia-Sun\u002FFL_FedAPA)] |\n| An Empirical Study of Federated Prompt Learning for Vision Language Model |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1188)] |\n| FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F706)] |\n| Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F733)] |\n| DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F744)] |\n| Backdoor Attack on Vertical Federated Graph Neural Network Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F877)] |\n| Federated Low-Rank Adaptation for Foundation Models: A Survey |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1196)] |\n| Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F761)] |\n| FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F770)] |\n| A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F771)] |\n| Federated Stochastic Bilevel Optimization with Fully First-Order Gradients |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F784)] |\n| AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F787)] |\n| Rethinking Federated Graph Learning: A Data Condensation Perspective |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F775)] |\n| MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F886)] |\n| Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F798)] |\n| Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F808)] |\n| Inconsistency-Based Federated Active Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F812)] |\n| Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fthakur25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAnshThakur\u002FFedMode)] |\n| FedBaF: Federated Learning Aggregation Biased by a Foundation Model |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fpark25b.html)] |\n| Global Group Fairness in Federated Learning via Function Tracking |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Frychener25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyvesrychener\u002FFair-FL)] |\n| On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fzeng25b.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdunzeng\u002FFedAWARE)] |\n| Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Flabbi25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLabbi-Safwan\u002FFed-UCBVI)] |\n| ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fozkara25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkazkara\u002Fadept)] |\n| Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkhellaf25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FRemiKhellaf\u002FFedCausal-RCTs-Khellaf\u002F)] |\n| The cost of local and global fairness in Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fduan25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpapersubmission678\u002FThe-cost-of-local-and-global-fairness-in-FL)] |\n| Federated Communication-Efficient Multi-Objective Optimization |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Faskin25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Faskinb\u002FFedCMOO)] |\n| Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fmangold25a.html)] [[CODE](https:\u002F\u002Fpmangold.fr\u002Fpapers\u002Ffed-richardson-romberg\u002Fsupplementary.zip)] |\n| Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fzhang25l.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FBernie0115\u002FLR-BPFL)] |\n| On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkeshri25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FSatishKeshri\u002FContinual_FL)] |\n| DPFL: Decentralized Personalized Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkharrat25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsalmakh1\u002FDPFL)] |\n| FedHM: Efficient federated learning for heterogeneous models via low-rank factorization |  | AI | 2025 | [[PUB](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0004370225000529)] |\n| Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34475)] |\n| Single-Loop Federated Actor-Critic across Heterogeneous Environments |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34469)] |\n| Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34468)] |\n| FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34464)] |\n| FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34459)] |\n| ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34449)] |\n| Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34438)] |\n| Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33455)] |\n| Asynchronous Federated Clustering with Unknown Number of Clusters |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34429)] |\n| Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34415)] |\n| HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34409)] |\n| LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34404)] |\n| Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33440)] |\n| Modeling Inter-Intra Heterogeneity for Graph Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34378)] |\n| pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34368)] |\n| First-Order Federated Bilevel Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34355)] |\n| GAS: Generative Activation-Aided Asynchronous Split Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35503)] |\n| FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35497)] |\n| Federated Graph Condensation with Information Bottleneck Principles |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33417)] |\n| A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract) |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35318)] |\n| Federated Learning with Sample-level Client Drift Mitigation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35480)] |\n| Pilot: Building the Federated Multimodal Instruction Tuning Framework |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35476)] |\n| Flexible Sharpness-Aware Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35475)] |\n| MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32076)] |\n| PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35460)] |\n| Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35458)] |\n| Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35444)] |\n| Federated Recommendation with Explicitly Encoding Item Bias |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33395)] |\n| Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34733)] |\n| Decentralized Federated Learning with Model Caching on Mobile Agents |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35429)] |\n| Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35426)] |\n| FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35423)] |\n| Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35409)] |\n| Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35405)] |\n| Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35398)] |\n| Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32807)] |\n| Reputation-aware Revenue Allocation for Auction-based Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34296)] |\n| Learn How to Query from Unlabeled Data Streams in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34287)] |\n| Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract) |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35304)] |\n| Graph Consistency and Diversity Measurement for Federated Multi-View Clustering |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34277)] |\n| WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34272)] |\n| Label-Free Backdoor Attacks in Vertical Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34246)] |\n| Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35054)] |\n| FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34228)] |\n| Fair Federated Survival Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34214)] |\n| Federated t-SNE and UMAP for Distributed Data Visualization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34204)] |\n| Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34201)] |\n| Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34197)] |\n| In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34192)] |\n| Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34187)] |\n| Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33572)] |\n| Federated Unlearning with Gradient Descent and Conflict Mitigation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34181)] |\n| Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32671)] |\n| FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34172)] |\n| FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34129)] |\n| Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34126)] |\n| FedCross: Intertemporal Federated Learning Under Evolutionary Games |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34104)] |\n| Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34094)] |\n| SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34090)] |\n| Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33328)] |\n| Federated Graph-Level Clustering Network |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34077)] |\n| LiD-FL: Towards List-Decodable Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34072)] |\n| Convergence Analysis of Federated Learning Methods Using Backward Error Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34060)] |\n| Progressive Distribution Matching for Federated Semi-Supervised Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32551)] |\n| TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34053)] |\n| Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34047)] |\n| EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34046)] |\n| FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34007)] |\n| pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33980)] |\n| FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33975)] |\n| FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33942)] |\n| Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35332)] |\n| PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33912)] |\n| TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33524)] |\n| FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33878)] |\n| DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33877)] |\n| Federated Assemblies |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33520)] |\n| Federated Causally Invariant Feature Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33866)] |\n| A New Federated Learning Framework Against Gradient Inversion Attacks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33865)] |\n| Exploring Vacant Classes in Label-Skewed Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33864)] |\n| Capture Global Feature Statistics for One-Shot Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33862)] |\n| Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33839)] |\n| MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32313)] |\n| Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33830)] |\n| Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33829)] |\n| Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33822)] |\n| SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33820)] |\n| Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33814)] |\n| How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33788)] |\n| Attribute Inference Attacks for Federated Regression Tasks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33787)] |\n| Federated Binary Matrix Factorization Using Proximal Optimization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33773)] |\n| Creating Coherence in Federated Non-Negative Matrix Factorization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33772)] |\n| Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33764)] |\n| DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33746)] |\n| Federated Foundation Models on Heterogeneous Time Series |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33739)] |\n| FedPop: Federated Population-based Hyperparameter Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33732)] |\n| Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35005)] |\n| EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33700)] |\n| Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33678)] |\n| Federated Multi-View Clustering via Tensor Factorization |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F438)] |\n| Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F439)] |\n| LG-FGAD: An Effective Federated Graph Anomaly Detection Framework |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F416)] |\n| Federated Prompt Learning for Weather Foundation Models on Devices |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F638)] |\n| Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F419)] |\n| Unlearning during Learning: An Efficient Federated Machine Unlearning Method |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F446)] |\n| Practical Hybrid Gradient Compression for Federated Learning Systems |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F458)] |\n| Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F450)] [[CODE](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedACD)] |\n| Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F457)] [[CODE](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedACD)] |\n| Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F788)] |\n| FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F501)] |\n| DarkFed: A Data-Free Backdoor Attack in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F491)] |\n| Scalable Federated Unlearning via Isolated and Coded Sharding |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F503)] |\n| Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F238)] |\n| Label Leakage in Vertical Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F902)] |\n| The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F980)] |\n| LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F515)] |\n| EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F51)] |\n| Knowledge Distillation in Federated Learning: A Practical Guide |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F905)] |\n| FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F526)] |\n| FedPFT: Federated Proxy Fine-Tuning of Foundation Models |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F531)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpzp-dzd\u002FFedPFT)] |\n| A Systematic Survey on Federated Semi-supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F911)] |\n| Intelligent Agents for Auction-based Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F912)] |\n| A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F552)] |\n| Dual Calibration-based Personalised Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F551)] |\n| Stakeholder-oriented Decision Support for Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F972)] |\n| Redefining Contributions: Shapley-Driven Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F554)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftnurbek\u002Fshapfed}{https:\u002F\u002Fgithub.com\u002Ftnurbek\u002Fshapfed)] |\n| A Survey on Efficient Federated Learning Methods for Foundation Model Training |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F919)] |\n| From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F575)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002FFFL4MIA)] |\n| FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F585)] |\n| FedFa: A Fully Asynchronous Training Paradigm for Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F584)] |\n| FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F594)] |\n| FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F599)] |\n| Personalized Federated Learning for Cross-City Traffic Prediction |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F611)] |\n| Federated Adaptation for Foundation Model-based Recommendations |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F603)] |\n| BADFSS: Backdoor Attacks on Federated Self-Supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F61)] |\n| Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F290)] [[CODE](https:\u002F\u002Fgithub.com\u002FGuogangZhu\u002FFedDB)] |\n| FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F632)] |\n| BOBA: Byzantine-Robust Federated Learning with Label Skewness | UIUC | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fbao24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12932)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002FBOBA)] |\n| Federated Linear Contextual Bandits with Heterogeneous Clients | University of Virginia | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fblaser24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.00116)] [[CODE](https:\u002F\u002Fgithub.com\u002Fblaserethan\u002FHetoFedBandit)] |\n| Federated Experiment Design under Distributed Differential Privacy | Stanford University; Meta | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fchen24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.04375)] [[CODE](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g\u002Fview?usp=drive_link)] |\n| Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression | Princeton University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fchen24d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19059)] |\n| Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Feven24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00465)] |\n| SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Ffraboni24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.11656)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning\u002Ftree\u002FSIFU)] |\n| Compression with Exact Error Distribution for Federated Learning | École Polytechnique | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fhegazy24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.20682)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmahegz\u002FCompWithExactError)] |\n| Adaptive Federated Minimax Optimization with Lower Complexities | NJU; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fhuang24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07303)] |\n| Adaptive Compression in Federated Learning via Side Information | Stanford University; University of Padova | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fisik24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.12625)] [[CODE](https:\u002F\u002Fgithub.com\u002FFrancescoPase\u002FFederated-KLMS)] |\n| On-Demand Federated Learning for Arbitrary Target Class Distributions | UNIST | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fjeong24a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Feai-lab\u002FOn-DemandFL)] |\n| FedFisher: Leveraging Fisher Information for One-Shot Federated Learning | CMU | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fjhunjhunwala24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.12329)] [[CODE](https:\u002F\u002Fgithub.com\u002FDivyansh03\u002FFedFisher)] |\n| Queuing dynamics of asynchronous Federated Learning | Huawei | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fleconte24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.00017)] |\n| Personalized Federated X-armed Bandit | Purdue University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fli24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.16323)] [[CODE](https:\u002F\u002Fgithub.com\u002FWilliamLwj\u002FPyXAB)] |\n| Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks | University of Oxford | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fmolaei24a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAnshThakur\u002FFL4HeterogenousEHRs)] |\n| Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization | University of Virginia | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fshen24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00944)] |\n| Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters | Northwestern University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fsun24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03824)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffedcodexx\u002FGeneralization-of-Federated-Learning)] |\n| Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains | Sofia University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Ftsoy24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.06672)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnikita-tsoy98\u002Fmutually-beneficial-federated-learning-replication)] |\n| Analysis of Privacy Leakage in Federated Large Language Models | University of Florida | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fvu24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.04784)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvunhatminh\u002FFL_Attacks.git)] |\n| Invariant Aggregator for Defending against Federated Backdoor Attacks | UIUC | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fwang24e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01834)] [[CODE](https:\u002F\u002Fgithub.com\u002FXiaoyang-Wang\u002FInvariantAggregator)] |\n| Communication-Efficient Federated Learning With Data and Client Heterogeneity | ISTA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fzakerinia24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10032)] [[CODE](https:\u002F\u002Fgithub.com\u002FShayanTalaei\u002FQuAFL)] |\n| FedMut: Generalized Federated Learning via Stochastic Mutation | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29146)] |\n| Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization | Carleton University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29562)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93915-federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] |\n| No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation | IIT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28950)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93775-no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10080)] [[CODE](https:\u002F\u002Fgithub.com\u002Fanujksirohi\u002FF2PGNN-AAAI24)] |\n| Formal Logic Enabled Personalized Federated Learning through Property Inference | Vanderbilt University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28962)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.07448)] |\n| Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks | Illinois Tech | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28965)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91722-task-agnostic-privacy-preserving-representation-learning-for-federated-learning-against-attribute-inference-attacks)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06989)] [[CODE](https:\u002F\u002Fgithub.com\u002FTAPPFL\u002FTAPPFL)] |\n| FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning | Leibniz University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28971)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93537-fairtrade-achieving-pareto-optimal-trade-offs-between-balanced-accuracy-and-fairness-in-federated-learning)] |\n| Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators | HKUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28974)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92397-combating-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05358)] |\n| Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity | UT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29025)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93417-fed-qssl-a-framework-for-personalized-federated-learning-under-bitwidth-and-data-heterogeneity)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13380)] |\n| On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning | University of Virginia | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29010)] |\n| FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning | LMU Munich Siemens AG | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29007)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91710-feddat-an-approach-for-foundation-model-finetuning-in-multi-modal-heterogeneous-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12305)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaokunChen245\u002FFedDAT)] |\n| Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models | Xi'an Jiaotong University Shaanxi Joint Key Laboratory for Artificial Intelligence | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29012)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91776-watch-your-head-assembling-projection-heads-to-save-the-reliability-of-federated-models)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.16255)] |\n| FedGCR: Achieving Performance and Fairness for  Federated Learning with Distinct Client Types via Group Customization  and Reweighting | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29031)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92275-fedgcr-achieving-performance-and-fairness-for-federated-learning-with-distinct-client-types-via-group-customization-and-reweighting)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcelinezheng\u002Ffedgcr)] |\n| Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation | Xiamen University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27909)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91824-federated-modality-specific-encoders-and-multimodal-anchors-for-personalized-brain-tumor-segmentation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.11803)] [[CODE](https:\u002F\u002Fgithub.com\u002Fqdaiing\u002Ffedmema)] |\n| Exploiting Label Skews in Federated Learning with Model Concatenation | NUS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29063)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92569-exploiting-label-skews-in-federated-learning-with-model-concatenation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06290)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsjtudyq\u002FFedConcat)] |\n| Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning | SUST; HKUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29958)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92937-complementary-knowledge-distillation-for-robust-and-privacy-preserving-model-serving-in-vertical-federated-learning)] |\n| Federated Learning via Input-Output Collaborative Distillation | University at Buffalo; USA Harvard Medical School | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30209)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94089-federated-learning-via-input-output-collaborative-distillation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.14478)] [[CODE](https:\u002F\u002Fgithub.com\u002Flsl001006\u002Ffediod)] |\n| Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space | University of Waterloo Vector Institute | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29122)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92727-calibrated-one-round-federated-learning-with-bayesian-inference-in-the-predictive-space)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.09817)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhasanmohsin\u002FbetaPredBayesFL)] |\n| FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning | HFUT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29113)] [[PDF](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedCSL)] |\n| FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning | Xi'an Jiaotong University;  Leiden University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29179)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92327-fedfixer-mitigating-heterogeneous-label-noise-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.16561)] |\n| FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing | NJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29181)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93122-fedlps-heterogeneous-federated-learning-for-multiple-tasks-with-local-parameter-sharing)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.08578)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjyzgh\u002FFedLPS)] |\n| Provably Convergent Federated Trilevel Learning | TJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29190)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.11835)] |\n| Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts | U-M | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29191)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93963-performative-federated-learning-a-solution-to-model-dependent-and-heterogeneous-distribution-shifts)] |\n| General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized  Services of Multi-Merchants | Kyung Hee University;  Harex InfoTech | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30309)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91475-general-commerce-intelligence-glocally-federated-nlp-based-engine-for-privacy-preserving-and-sustainable-personalized-services-of-multi-merchants)] |\n| EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning | Sony AI | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29258)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91709-emgan-early-mix-gan-on-extracting-server-side-model-in-split-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlijingtao\u002FSFL-MEA)] |\n| FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels | SYSU; HKU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28095)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91764-feddiv-collaborative-noise-filtering-for-federated-learning-with-noisy-labels)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12263)] [[CODE](https:\u002F\u002Fgithub.com\u002Flijichang\u002FFLNL-FedDiv)] |\n| Point Transformer with Federated Learning for  Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained  Whole Slide Images | USTC; CAS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28082)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92706-point-transformer-with-federated-learning-for-predicting-breast-cancer-her2-status-from-hematoxylin-and-eosin-stained-whole-slide-images)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06454)] [[CODE](https:\u002F\u002Fgithub.com\u002FBoyden\u002FPointTransformerFL)] |\n| FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning | CAS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29254)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.02734)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsuperlj666\u002Ffedns)] |\n| Federated X-armed Bandit | Purdue University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29267)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93049-federated-x-armed-bandit)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15268)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwilliamlwj\u002Fpyxab)] |\n| Algorithmic Foundation of Federated Learning with Sequential Data | GMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30291)] |\n| UFDA: Universal Federated Domain Adaptation with Practical Assumptions | XJTU; University of Sydney | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29311)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93578-ufda-universal-federated-domain-adaptation-with-practical-assumptions)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.15570)] [[CODE](https:\u002F\u002Fgithub.com\u002FXinhui-99\u002FUFDA)] |\n| FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update | Hithink RoyalFlush Information Network Co | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29297)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92855-fedasmu-efficient-asynchronous-federated-learning-with-dynamic-staleness-aware-model-update)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05770)] |\n| Language-Guided Transformer for Federated Multi-Label Classification | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29295)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93447-language-guided-transformer-for-federated-multi-label-classification)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.07165)] [[CODE](https:\u002F\u002Fgithub.com\u002FJack24658735\u002FFedLGT)] |\n| FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers | SZU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28175)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92166-fedcd-federated-semi-supervised-learning-with-class-awareness-balance-via-dual-teachers)] [[CODE](https:\u002F\u002Fgithub.com\u002FYunzZ-Liu\u002FFedCD\u002F)] |\n| Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning | HEU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30131)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94230-beyond-traditional-threats-a-persistent-backdoor-attack-on-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002FPhD-TaoLiu\u002FFCBA)] |\n| Federated Learning with Extremely Noisy Clients via Negative Distillation | XMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29329)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93309-federated-learning-with-extremely-noisy-clients-via-negative-distillation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12703)] [[CODE](https:\u002F\u002Fgithub.com\u002FlinChen99\u002FFedNed)] |\n| FedST: Federated Style Transfer Learning for Non-IID Image Segmentation | USTB | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28199)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93609-fedst-federated-style-transfer-learning-for-non-iid-image-segmentation)] [[学报](https:\u002F\u002Fjournal.bupt.edu.cn\u002FCN\u002Fabstract\u002Fabstract5178.shtml)] [[CODE](https:\u002F\u002Fgithub.com\u002FYoferChen\u002FFedST)] |\n| PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning | USTC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29339)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92243-ppidsg-a-privacy-preserving-image-distribution-sharing-scheme-with-gan-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10380)] [[CODE](https:\u002F\u002Fgithub.com\u002Fytingma\u002FPPIDSG)] |\n| A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities | DCU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30400)] |\n| A Primal-Dual Algorithm for Hybrid Federated Learning | Northwestern University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29363)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93144-a-primal-dual-algorithm-for-hybrid-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08106)] |\n| FedLF: Layer-Wise Fair Federated Learning | CUHK; Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29368)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93087-fedlf-layer-wise-fair-federated-learning)] |\n| Towards Fair Graph Federated Learning via Incentive Mechanisms | ZJU; FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29365)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92583-towards-fair-graph-federated-learning-via-incentive-mechanisms)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13306)] [[CODE](https:\u002F\u002Fgithub.com\u002FChenglu0426\u002FFairGraphFL)] |\n| Towards the Robustness of Differentially Private Federated Learning | THU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29967)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92491-towards-the-robustness-of-differentially-private-federated-learning)] |\n| Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective | ZJU; HUAWEI | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29385)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94020-resisting-backdoor-attacks-in-federated-learning-via-bidirectional-elections-and-individual-perspective)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.16456)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenqincn\u002FSnowball)] |\n| Integer Is Enough: When Vertical Federated Learning Meets Rounding | ZJU; Ant Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29388)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93362-integer-is-enough-when-vertical-federated-learning-meets-rounding)] |\n| CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data | XMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29416)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92441-clip-guided-federated-learning-on-heterogeneity-and-long-tailed-data)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.08648)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshijiangming1\u002FCLIP2FL)] |\n| Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning | FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29434)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92772-federated-adaptive-prompt-tuning-for-multi-domain-collaborative-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07864)] [[CODE](https:\u002F\u002Fgithub.com\u002Fleondada\u002Ffedapt)] |\n| Multi-Dimensional Fair Federated Learning | SDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29430)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92619-multi-dimensional-fair-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05551)] |\n| HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation | ENN Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30317)] |\n| On the Role of Server Momentum in Federated Learning | University of Virginia | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29439)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12670)] |\n| FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants | BUPT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29446)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93158-fedcompetitors-harmonious-collaboration-in-federated-learning-with-competing-participants)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.11391)] |\n| z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning | CUHK; China Shenzhen Research Institute of Big Data | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29454)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93975-z-signfedavg-a-unified-stochastic-sign-based-compression-for-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.02589)] |\n| Data Disparity and Temporal Unavailability Aware  Asynchronous Federated Learning for Predictive Maintenance on  Transportation Fleets | Volkswagen Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29467)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92405-data-disparity-and-temporal-unavailability-aware-asynchronous-federated-learning-for-predictive-maintenance-on-transportation-fleets)] |\n| Federated Graph Learning under Domain Shift with Generalizable Prototypes | WHU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29468)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92526-federated-graph-learning-under-domain-shift-with-generalizable-prototypes)] |\n| TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients | Technical University of Munich | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29481)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91900-turbosvm-fl-boosting-federated-learning-through-svm-aggregation-for-lazy-clients)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.12012)] [[CODE](https:\u002F\u002Fgithub.com\u002FKasneci-Lab\u002FTurboSVM-FL)] |\n| Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization | TJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29510)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.10272)] [[CODE](https:\u002F\u002Fgithub.com\u002Fweiyikang\u002FFedGM)] |\n| Concealing Sensitive Samples against Gradient Leakage in Federated Learning | Monash University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30171)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94147-concealing-sensitive-samples-against-gradient-leakage-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05724)] [[CODE](https:\u002F\u002Fgithub.com\u002FJingWu321\u002FDCS-2)] |\n| FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise | HUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29525)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92926-feda3i-annotation-quality-aware-aggregation-for-federated-medical-image-segmentation-against-heterogeneous-annotation-noise)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12838)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002FFedAAAI)] |\n| Federated Causality Learning with Explainable Adaptive Optimization | SDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29566)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93217-federated-causality-learning-with-explainable-adaptive-optimization)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05540)] |\n| Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users | USTC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30045)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93664-federated-contextual-cascading-bandits-with-asynchronous-communication-and-heterogeneous-users)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.16312)] |\n| Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models | FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29568)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.04063)] |\n| Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification | XMU; University of Trento | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28468)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91850-diversity-authenticity-co-constrained-stylization-for-federated-domain-generalization-in-person-re-identification)] |\n| PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search | U of T | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29576)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92749-perfedrlnas-one-for-all-personalized-federated-neural-architecture-search)] |\n| Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction | BUPT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29603)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92183-efficient-asynchronous-federated-learning-with-prospective-momentum-aggregation-and-fine-grained-correction)] |\n| Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms | HKU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27796)] |\n| FedTGP: Trainable Global Prototypes with  Adaptive-Margin-Enhanced Contrastive Learning for Data and Model  Heterogeneity in Federated Learning | SJTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29617)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91976-fedtgp-trainable-global-prototypes-with-adaptive-margin-enhanced-contrastive-learning-for-data-and-model-heterogeneity-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.03230)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedTGP)] |\n| LR-XFL: Logical Reasoning-Based Explainable Federated Learning | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30179)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12681)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyanci87\u002Flr-xfl)] |\n| A Huber Loss Minimization Approach to Byzantine Robust Federated Learning | Zhejiang Lab | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30181)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94170-a-huber-loss-minimization-approach-to-byzantine-robust-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12581)] |\n| Knowledge-Aware Parameter Coaching for Personalized Federated Learning | Northeastern University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29651)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92711-knowledge-aware-parameter-coaching-for-personalized-federated-learning)] |\n| Federated Label-Noise Learning with Local Diversity Product Regularization | SJTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29659)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92697-federated-label-noise-learning-with-local-diversity-product-regularization)] [[SUPP](https:\u002F\u002Fwanglab.sjtu.edu.cn\u002Fuserfiles\u002Ffiles\u002FSupp_FedLNL.pdf)] |\n| Adapted Weighted Aggregation in Federated Learning (Student Abstract) | UBC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30557)] |\n| Knowledge Transfer via Compact Model in Federated Learning (Student Abstract) | University of Sydney | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30498)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91519-knowledge-transfer-via-compact-model-in-federated-learning-student-abstract)] |\n| PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract) | The Ohio State University Auton Lab, CMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30438)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91585-picsr-prototype-informed-cross-silo-router-for-federated-learning-student-abstract)] |\n| Privacy-preserving graph convolution network for federated item recommendation | SZU | AI | 2023 | [[PUB](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS000437022300142X)] |\n| Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation | CAS; UCAS; JD Technology; JD Intelligent Cities Research | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25531)] |\n| Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense | USTC; State Key Laboratory of Cognitive Intelligence | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25611)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.05399)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyflyl613\u002Ffedrec)] |\n| Incentive-Boosted Federated Crowdsourcing | SDU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25744)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14439)] |\n| Tackling Data Heterogeneity in Federated Learning with Class Prototypes | Lehigh University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25891)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02758)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyutong-dai\u002Ffednh)] |\n| FairFed: Enabling Group Fairness in Federated Learning | USC | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25911)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00857)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F613201113)] |\n| Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning | MSU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25955)] |\n| Complement Sparsification: Low-Overhead Model Pruning for Federated Learning | NJIT | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25977)] |\n| Almost Cost-Free Communication in Federated Best Arm Identification | NUS | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26010)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.09215)] |\n| Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning | University of Southern California Inha University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26023)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10302)] |\n| Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning | BJTU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26083)] |\n| FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26122)] |\n| Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning | USC | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26177)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.03328)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38960185\u002Fsecuring-secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nVV6S2sb_UL&name=supplementary_material)] |\n| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26187)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13009)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedstar)] |\n| Efficient  Distribution Similarity Identification in Clustered Federated Learning  via Principal Angles between Client Data Subspaces | UCSD | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26197)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.10526)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmmorafah\u002Fpacfl)] |\n| FedABC: Targeting Fair Competition in Personalized Federated Learning | WHU; Hubei Luojia Laboratory; JD Explore Academy | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26203)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.07450)] |\n| Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework | SUTD | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26212)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01519)] |\n| FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability | XMU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26223)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwwzzz\u002Ffedgs)] |\n| Faster Adaptive Federated Learning | University of Pittsburgh | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26235)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.00974)] |\n| FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation | HKUST | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26237)] [[CODE](https:\u002F\u002Fgithub.com\u002FCodePothunter\u002Ffednp)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3XM_NNvXCBo)] [[SUPP](https:\u002F\u002Fgithub.com\u002FCodePothunter\u002Ffednp\u002Fblob\u002Fmain\u002Fappendix.pdf)] |\n| Bayesian Federated Neural Matching That Completes Full Information | TJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26245)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.08010)] |\n| CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems | ZJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26246)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14216)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxjiajiahao\u002Ffederated-minimax)] |\n| Federated Generative Model on Multi-Source Heterogeneous Data in IoT | GSU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26252)] |\n| DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness | SUNY-Binghamton University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26271)] |\n| FedALA: Adaptive Local Aggregation for Personalized Federated Learning | SJTU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26330)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01197)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedala)] |\n| Delving into the Adversarial Robustness of Federated Learning | ZJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26331)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09479)] |\n| On the Vulnerability of Backdoor Defenses for Federated Learning | TJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26393)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08170)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjinghuichen\u002Ffocused-flip-federated-backdoor-attack)] |\n| Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model | RUC; Engineering Research Center of Ministry of Education on Database and BI | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26400)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.05516)] |\n| DPAUC: Differentially Private AUC Computation in Federated Learning | ByteDance Inc. | AAAI Special Tracks | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26770)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12294)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Ffedlearner)] |\n| Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout | NTU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26836)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.11485)] |\n| Industry-Scale Orchestrated Federated Learning for Drug Discovery | KU Leuven | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26847)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08871)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=J_RmZhKzBcA)] |\n| A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract) | CNU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26984)] |\n| MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract) | PolyU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26995)] |\n| Clustered Federated Learning for Heterogeneous Data (Student Abstract) | RUC | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27049)] |\n| FedSampling: A Better Sampling Strategy for Federated Learning | THU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F462)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.14245)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftaoqi98\u002FFedSampling)] |\n| HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F440)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.14384)] |\n| FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning | NTU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F394)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.05174)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcyyever\u002Fdistributed_learning_simulator)] |\n| Federated Probabilistic Preference Distribution Modelling with  Compactness Co-Clustering for Privacy-Preserving Multi-Domain  Recommendation | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F245)] |\n| Federated Graph Semantic and Structural Learning | WHU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F426)] |\n| BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning | SYSU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F498)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05221)] |\n| FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment | SYSU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F444)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06124)] |\n| FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation | Webank | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F418)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12623)] |\n| Globally Consistent Federated Graph Autoencoder for Non-IID Graphs | FZU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F419)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgcfgae\u002FGCFGAE)] |\n| Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning | NTU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F474)] |\n| Dual Personalization on Federated Recommendation | JLU; University of Technology Sydney | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F507)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08143)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhangcx19\u002Fijcai-23-pfedrec)] |\n| FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity | HUST | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F492)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05230)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002Ffednoro)] |\n| Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning | Xiangtan University | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F508)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10783)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhanghangtao\u002Fpoisoning-attack-on-fl)] |\n| FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks | CUHK | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F412)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.09729)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcynricfu\u002Ffedhgn)] |\n| FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer | Ping An Technology; THU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F443)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.15347)] |\n| Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data | UTS | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F393)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09152)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshengchaochen82\u002Fmetepfl)] |\n| FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F483)] [[CODE](https:\u002F\u002Fgithub.com\u002FHanzhouu\u002FFedBFPT)] |\n| Bayesian Federated Learning: A Survey |  | IJCAI Survey Track | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13267)] |\n| A Survey of Federated Evaluation in Federated Learning | Macquarie University | IJCAI Survey Track | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F758)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.08070)] |\n| SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract) | INSA Centre Val de Loire | IJCAI Journal Track | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F772)] |\n| The communication cost of security and privacy in federated frequency estimation | Stanford | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fchen23e.html)] [[CODE](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1A3sp42a4RKswxjCOBAXlfUxBzL5IF431?usp=share_link)] |\n| Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout | Rice University | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fdun23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdunchen\u002FAsyncDrop__Release)] |\n| Federated Learning under Distributed Concept Drift | CMU | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fjothimurugesan23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FFedDrift)] |\n| Characterizing Internal Evasion Attacks in Federated Learning | CMU | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fkim23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftj-kim\u002FpFedDef_v1)] |\n| Federated Asymptotics: a model to compare federated learning algorithms | Stanford | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fcheng23b.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryxcheng\u002Fpersonalized-federated-learning)] |\n| Private Non-Convex Federated Learning Without a Trusted Server | USC | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Flowy23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fghafeleb\u002FPrivate-NonConvex-Federated-Learning-Without-a-Trusted-Server)] |\n| Federated Learning for Data Streams | Universit ́ e Cˆ ote d’Azur | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fmarfoq23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fstreaming-fl)] |\n| Nothing but Regrets — Privacy-Preserving Federated Causal Discovery | Helmholtz Centre for Information Security | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fmian23a.html)] [[CODE](https:\u002F\u002Feda.rg.cispa.io\u002Fprj\u002Fperi\u002F)] |\n| Active Membership Inference Attack under Local Differential Privacy in Federated Learning | UFL | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fnguyen23e.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftrucndt\u002Fami)] |\n| Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms | CMAP | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fplassier23a.html)] |\n| Byzantine-Robust Federated Learning with Optimal Statistical Rates | UC Berkeley | AISTATS | 2023 | [[PUB](https:\u002F\u002Fgithub.com\u002Fwanglun1996\u002Fsecure-robust-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwanglun1996\u002Fsecure-robust-federated-learning)] |\n| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13009)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedstar)] |\n| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwwzzz\u002Ffedgs)] |\n| Incentive-boosted Federated Crowdsourcing | SDU | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14439)] |\n| Towards Understanding Biased Client Selection in Federated Learning. | CMU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjee-cho22a.html)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjee-cho22a\u002Fjee-cho22a-supp.zip)] |\n| FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning | KAUST | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fgasanov22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.11556)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fgasanov22a\u002Fgasanov22a-supp.zip)] |\n| Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. | Stanford | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fglasgow22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.03741)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002Fsharp-bounds-for-fedavg-and-continuous-perspective)] |\n| Federated Reinforcement Learning with Environment Heterogeneity. | PKU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjin22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.02634)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpengyang7881187\u002Ffedrl)] |\n| Federated Myopic Community Detection with One-shot Communication | Purdue | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fke22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07255)] |\n| Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. | University of Virginia | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fli22e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.01463)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcyrilli\u002FAsync-LinUCB)] |\n| Towards Federated Bayesian Network Structure Learning with Continuous Optimization. | CMU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fng22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.09356)] [[CODE](https:\u002F\u002Fgithub.com\u002Fignavierng\u002Fnotears-admm)] |\n| Federated Learning with Buffered Asynchronous Aggregation | Meta AI | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fnguyen22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06639)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ui-OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar)] |\n| Differentially Private Federated Learning on Heterogeneous Data. | Stanford | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fnoble22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09278)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmaxencenoble\u002FDifferential-Privacy-for-Heterogeneous-Federated-Learning)] |\n| SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification | Princeton | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fpanda22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.06274)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsparsefed\u002Fsparsefed)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TXG7ZScheas&ab_channel=GoogleTechTalks)] |\n| Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning | KAUST | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fqian22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.01847)] |\n| Federated Functional Gradient Boosting. | University of Pennsylvania | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fshen22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06972)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FFederated-Learning-Pytorch)] |\n| QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. | Criteo AI Lab | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fvono22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00797)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fvono22a\u002Fvono22a-supp.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar)] |\n| Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting **`kg.`** | ZJU | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F273)] [[PDF](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2205.04692)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzjukg\u002Fmaker)] |\n| Personalized Federated Learning With a Graph | UTS | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F357)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.00829)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdawenzi098\u002FSFL-Structural-Federated-Learning)] |\n| Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification | ZJU | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F272)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11903)] |\n| Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F301)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.08394)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljaiverson\u002FpFL-APPLE)] |\n| Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F399)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.12703)] |\n| Private Semi-Supervised Federated Learning. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F279)] |\n| Continual Federated Learning Based on Knowledge Distillation. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.24963\u002Fijcai.2022\u002F306)] |\n| Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F308)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13399)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshangxinyi\u002FCReFF-FL)] |\n| Federated Multi-Task Attention for Cross-Individual Human Activity Recognition |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F475)] |\n| Personalized Federated Learning with Contextualized Generalization. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F311)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13044)] |\n| Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F106)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13256)] |\n| FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F324)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.08211)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederatedAI\u002Fresearch\u002Ftree\u002Fmain\u002Fpublications\u002FFedCG)] |\n| FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F385)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.11536)] |\n| Towards Verifiable Federated Learning **`surv.`** |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F792)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08310)] |\n| HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images | CUHK; BUAA | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F19993)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.10775)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmed-air\u002FHarmoFL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F472555067)] |\n| Federated Learning for Face Recognition with Gradient Correction | BUPT | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20095)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.07246)] |\n| SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data | USC | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20643)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02743)] [[CODE](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FSpreadGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F429720860)] |\n| SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures | HIT; PCL | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20345)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwudonglei99\u002Fsmartidx)] |\n| Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network | TJU | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F19878)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.08831)] |\n| Seizing Critical Learning Periods in Federated Learning | SUNY-Binghamton University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20859)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05613)] |\n| Coordinating Momenta for Cross-silo Federated Learning | University of Pittsburgh | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20853)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03970)] |\n| FedProto: Federated Prototype Learning over Heterogeneous Devices | UTS | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20819)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.00243)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedproto)] |\n| FedSoft: Soft Clustered Federated Learning with Proximal Local Updating | CMU | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20785)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.06053)] [[CODE](https:\u002F\u002Fgithub.com\u002Fycruan\u002FFedSoft)] |\n| Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better | The University of Texas at Austin | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20555)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.09824)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbibikar\u002Ffeddst)] |\n| FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition | National Taiwan University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20057)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12496)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjackie840129\u002Ffedfr)] |\n| SplitFed: When Federated Learning Meets Split Learning | CSIRO | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20825)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12088)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchandra2thapa\u002FSplitFed-When-Federated-Learning-Meets-Split-Learning)] |\n| Efficient Device Scheduling with Multi-Job Federated Learning | Soochow University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F21235)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.05928)] |\n| Implicit Gradient Alignment in Distributed and Federated Learning | IIT Kanpur | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20597)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13897)] |\n| Federated Nearest Neighbor Classification with a Colony of Fruit-Flies | IBM Research | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20775)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.07157)] [[CODE](https:\u002F\u002Fgithub.com\u002Frithram\u002Fflynn)] |\n| Iterated Vector Fields and Conservatism, with Applications to Federated Learning. | Google | ALT | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv167\u002Fcharles22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.03973)] |\n| Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F202)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.01558)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35198)] |\n| Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F352)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12300)] |\n| FedSpeech: Federated Text-to-Speech with Continual Learning |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F527)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.07216)] |\n| Practical One-Shot Federated Learning for Cross-Silo Setting |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F205)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.01017)] [[CODE](https:\u002F\u002Fgithub.com\u002FQinbinLi\u002FFedKT)] |\n| Federated Model Distillation with Noise-Free Differential Privacy |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F216)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08310)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35184)] |\n| LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F217)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.15789)] |\n| Federated Learning with Fair Averaging. :fire: |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F223)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.14937)] [[CODE](https:\u002F\u002Fgithub.com\u002FWwZzz\u002FeasyFL)] |\n| H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F67)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00275)] |\n| Communication-efficient and Scalable Decentralized Federated Edge Learning. |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F720)] |\n| Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating | Xidian University; JD Tech | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17301)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00958)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947765\u002Fsecure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating)] |\n| FedRec++: Lossless Federated Recommendation with Explicit Feedback | SZU | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16546)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947798\u002Ffedrec-lossless-federated-recommendation-with-explicit-feedback)] |\n| Federated Multi-Armed Bandits | University of Virginia | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17156)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.12204)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenGroup\u002FFMAB)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947985\u002Ffederated-multiarmed-bandits)] |\n| On the Convergence of Communication-Efficient Local SGD for Federated Learning | Temple University; University of Pittsburgh | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16920)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948341\u002Fon-the-convergence-of-communicationefficient-local-sgd-for-federated-learning)] |\n| FLAME: Differentially Private Federated Learning in the Shuffle Model | Renmin University of China; Kyoto University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17053)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08063)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948496\u002Fflame-differentially-private-federated-learning-in-the-shuffle-model)] [[CODE](https:\u002F\u002Fgithub.com\u002FRachelxuan11\u002FFLAME)] |\n| Toward Understanding the Influence of Individual Clients in Federated Learning | SJTU; The University of Texas at Dallas | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17263)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.10936)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948549\u002Ftoward-understanding-the-influence-of-individual-clients-in-federated-learning)] |\n| Provably Secure Federated Learning against Malicious Clients | Duke University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16849)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01854)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LP4uqW18yA0&ab_channel=PurdueCERIAS)] [[SLIDE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002FSecure_Federated_Learning.pdf)] |\n| Personalized Cross-Silo Federated Learning on Non-IID Data | Simon Fraser University; McMaster University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16960)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.03797)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948676\u002Fpersonalized-crosssilo-federated-learning-on-noniid-data)] [[UC.](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FPFL-Non-IID)] |\n| Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation | Cornell University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16669)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00753)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkpdonahue\u002Fmodel_sharing_games)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948684\u002Fmodelsharing-games-analyzing-federated-learning-under-voluntary-participation)] |\n| Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning | University of Nevada; IBM Research | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17291)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.00655)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949098\u002Fcurse-or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning)] |\n| Game of Gradients: Mitigating Irrelevant Clients in Federated Learning | IIT Bombay; IBM Research | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17093)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.12257)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnlokeshiisc\u002Fsfedavg-aaai21)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949109\u002Fgame-of-gradients-mitigating-irrelevant-clients-in-federated-learning)] [[SUPP](https:\u002F\u002Fgithub.com\u002Fnlokeshiisc\u002FSFedAvg-AAAI21)] |\n| Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models | CUHK; Arizona State University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17240)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13900)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949195\u002Ffederated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models)] [[CODE](https:\u002F\u002Fgithub.com\u002FREIYANG\u002FFedBCD)] |\n| Addressing Class Imbalance in Federated Learning | Northwestern University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17219)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06217)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949283\u002Fadressing-class-imbalance-in-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbalanced-fl\u002FAddressing-Class-Imbalance-FL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F443009189)] |\n| Defending against Backdoors in Federated Learning with Robust Learning Rate | The University of Texas at Dallas | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17118)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.03767)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949344\u002Fdefending-against-backdoors-in-federated-learning-with-robust-learning-rate)] [[CODE](https:\u002F\u002Fgithub.com\u002FTinfoilHat0\u002FDefending-Against-Backdoors-with-Robust-Learning-Rate)] |\n| Free-rider Attacks on Model Aggregation in Federated Learning | Accenture Labs | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Ffraboni21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.11901)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27640)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Ffraboni21a\u002Ffraboni21a-supp.pdf)] |\n| Federated f-differential privacy | University of Pennsylvania | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fzheng21a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fenosair\u002Ffederated-fdp)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27595)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fzheng21a\u002Fzheng21a-supp.pdf)] |\n| Federated learning with compression: Unified analysis and sharp guarantees :fire: | The Pennsylvania State University; The University of Texas at Austin | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fhaddadpour21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.01154)] [[CODE](https:\u002F\u002Fgithub.com\u002FMLOPTPSU\u002FFedTorch)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27584)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fhaddadpour21a\u002Fhaddadpour21a-supp.pdf)] |\n| Shuffled Model of Differential Privacy in Federated Learning | UCLA; Google | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fgirgis21a.html)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27565)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fgirgis21a\u002Fgirgis21a-supp.pdf)] |\n| Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning | Google | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fcharles21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.05032)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27559)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fcharles21a\u002Fcharles21a-supp.pdf)] |\n| Federated Multi-armed Bandits with Personalization | University of Virginia; The Pennsylvania State University | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fshi21c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.13101)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenGroup\u002FPF_MAB)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27521)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fshi21c\u002Fshi21c-supp.pdf)] |\n| Towards Flexible Device Participation in Federated Learning | CMU; SYSU | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fruan21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.06954)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27467)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fruan21a\u002Fruan21a-supp.pdf)] |\n| Federated Meta-Learning for Fraudulent Credit Card Detection |  | IJCAI | 2020 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F642)] [[VIDEO](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002Fvideo\u002F23994)] |\n| A Multi-player Game for Studying Federated Learning Incentive Schemes |  | IJCAI | 2020 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F769)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbenggggggggg\u002Ffedgame)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F353868739)] |\n| Practical Federated Gradient Boosting Decision Trees | NUS; UWA | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5895)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04206)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FPrivML)] |\n| Federated Learning for Vision-and-Language Grounding Problems | PKU; Tencent | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6824)] |\n| Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework | BUAA | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6096)] |\n| Federated Patient Hashing | Cornell University | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6121)] |\n| Robust Federated Learning via Collaborative Machine Teaching | Symantec Research Labs; KAUST | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5826)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.02941)] |\n| FedVision: An Online Visual Object Detection Platform Powered by Federated Learning | WeBank | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F7021)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06202)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederatedAI\u002FFedVision)] |\n| FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization | UC Santa Barbara; UT Austin | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Freisizadeh20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.13014)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F7961)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Freisizadeh20a\u002Freisizadeh20a-supp.pdf)] |\n| How To Backdoor Federated Learning :fire: | Cornell Tech | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fbagdasaryan20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00459)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F8046)] [[CODE](https:\u002F\u002Fgithub.com\u002Febagdasa\u002Fbackdoor_federated_learning)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fbagdasaryan20a\u002Fbagdasaryan20a-supp.pdf)] |\n| Federated Heavy Hitters Discovery with Differential Privacy | RPI; Google | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fzhu20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.08534)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F8129)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fzhu20a\u002Fzhu20a-supp.pdf)] |\n| Multi-Agent Visualization for Explaining Federated Learning | WeBank | IJCAI | 2019 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F960)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FNPGf_OJrzOg)] |\n\n\u003C!-- END:fl-in-top-ai-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n\n## fl in top ml conference and journal\n\nFederated Learning papers accepted by top ML(machine learning) conference and journal, Including [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fnips\u002Findex.html)(Annual Conference on Neural Information Processing Systems), [ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficml\u002Findex.html)(International Conference on Machine Learning), [ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficlr\u002Findex.html)(International Conference on Learning Representations), [COLT](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fcolt\u002Findex.html)(Annual Conference Computational Learning Theory) , [UAI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fuai\u002Findex.html)(Conference on Uncertainty in Artificial Intelligence),[Machine Learning](https:\u002F\u002Fdblp.org\u002Fdb\u002Fjournals\u002Fml\u002Findex.html), [JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fjmlr\u002Findex.html)(Journal of Machine Learning Research), [TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fpami\u002Findex.html)(IEEE Transactions on Pattern Analysis and Machine Intelligence).\n\n- [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANeurIPS%3A) [2024](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2024)([OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2024\u002FConference#tab-accept-oral)), [2023](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2023)([OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2023\u002FConference#tab-accept-oral)), [2022](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2022)([OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2022\u002FConference)), [2021](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021)([OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2021\u002FConference)), [2020](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020), [2018](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018), [2017](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017)\n- [ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICML%3A) [2025](https:\u002F\u002Ficml.cc\u002FConferences\u002F2025\u002FSchedule?type=Poster), [2024](https:\u002F\u002Ficml.cc\u002FConferences\u002F2024\u002FSchedule?type=Poster), [2023](https:\u002F\u002Ficml.cc\u002FConferences\u002F2023\u002FSchedule?type=Poster), [2022](https:\u002F\u002Ficml.cc\u002FConferences\u002F2022\u002FSchedule?type=Poster), [2021](https:\u002F\u002Ficml.cc\u002FConferences\u002F2021\u002FSchedule?type=Poster), [2020](https:\u002F\u002Ficml.cc\u002FConferences\u002F2020\u002FSchedule?type=Poster), [2019](https:\u002F\u002Ficml.cc\u002FConferences\u002F2019\u002FSchedule?type=Poster)\n- [ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICLR%3A) [2025](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2025\u002FConference), [2024](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2024\u002FConference), [2023](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2023\u002FConference), [2022](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2022\u002FConference), [2021](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2021\u002FConference), [2020](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2020\u002FConference)\n- [COLT](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ACOLT%3A) [2023](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002F)\n- [UAI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AUAI%3A) [2025](https:\u002F\u002Fwww.auai.org\u002Fuai2025\u002Faccepted_papers), [2024](https:\u002F\u002Fwww.auai.org\u002Fuai2024\u002Faccepted_papers), [2023](https:\u002F\u002Fwww.auai.org\u002Fuai2023\u002Faccepted_papers), [2022](https:\u002F\u002Fwww.auai.org\u002Fuai2022\u002Faccepted_papers), [2021](https:\u002F\u002Fwww.auai.org\u002Fuai2021\u002Faccepted_papers)\n- [Machine Learning](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fml%3A) 2025, 2024, 2023, 2022\n- [JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fjmlr%3A) 2024([v25](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv25\u002F)), 2023([v24](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F)), 2021([v22](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv22\u002F))\n- [TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpami%3A) 2025, 2024, 2023, 2022\n\n\u003Cdetails open>\n\u003Csummary>fl in top ml conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-ml-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                     |    Venue             |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | ---- | ------------------------------------------------------------ |\n| Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fzhang25f.html)] |\n| FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Ftang25a.html)] |\n| FeDCM: Federated Learning of Deep Causal Generative Models |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Frahman25a.html)] |\n| Federated Rényi Fair Inference in Federated Heterogeneous System |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fma25a.html)] |\n| FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Flin25a.html)] |\n| ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fkaragulyan25a.html)] |\n| FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fibrahim25a.html)] |\n| Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fdiana25a.html)] |\n| Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fakgul25a.html)] |\n| Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off | SYSU | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=C7dmhyTDrx)] [[CODE](https:\u002F\u002Fgithub.com\u002F6lyc\u002FFedCEO_Collaborate-with-Each-Other)] |\n| Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wleRTUQj07)] |\n| SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j7H4mbeOI1)] [[CODE](https:\u002F\u002Fgithub.com\u002FNusIoraPrivacy\u002FSecEmb)] |\n| Causality Inspired Federated Learning for OOD Generalization |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pWWUJw2qew)] |\n| Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based \tStochastic Controlled Weight Averaging |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HqmXiuFaOr)] [[CODE](https:\u002F\u002Fgithub.com\u002FjunkangLiu0\u002FFedSWA)] |\n| One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PqJFVbJAMR)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingzhaoYang\u002FFedLMG)] |\n| FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XCLZgbm99O)] |\n| An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eLkkXaPFEP)] [[CODE](https:\u002F\u002Fgithub.com\u002F5Martina5\u002FESFMC)] |\n| Gap-Dependent Bounds for Federated $Q$-Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0n2nXmOxZS)] |\n| FedBEns: One-Shot Federated Learning based on Bayesian Ensemble |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oTCiv1bkjG)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjacopot96\u002FFedBEns)] |\n| NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hC7zCFk5Dp)] [[CODE](https:\u002F\u002Fgithub.com\u002FGabe-Thomp\u002Fntk-dfl)] |\n| Federated Learning for Feature Generalization with Convex Constraints |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pI4AbQ7pg1)] [[CODE](https:\u002F\u002Fgithub.com\u002Fskku-dhkim\u002FFedTorch.git)] |\n| Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EU5lci90fF)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdestiny301\u002Fuefl)] |\n| Towards Trustworthy Federated Learning with Untrusted Participants |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PjadKnUson)] |\n| Multi-Session Budget Optimization for Forward Auction-based Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bFB0N8ABIr)] |\n| Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0p86Mhg014)] [[CODE](https:\u002F\u002Fgithub.com\u002FMoratalYang\u002FFedDDA)] |\n| LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=li59703WbA)] |\n| Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cgHfR7bt0V)] |\n| Differentially Private Federated $k$-Means Clustering with Server-Side Data |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EFLPHl5RGJ)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjonnyascott\u002Ffed-dp-kmeans)] |\n| CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lvkVhZ776k)] |\n| Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MM6ZWF7gl9)] [[CODE](https:\u002F\u002Fgithub.com\u002FZLHe0\u002Ffedclup)] |\n| $S^2$FGL: Spatial Spectral Federated Graph Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pFQ3MnyIT6)] [[CODE](https:\u002F\u002Fgithub.com\u002FWonder7racer\u002FS2FGL.git)] |\n| FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HnwcrtoDd4)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsrijith1996\u002FFSL-SAGE)] |\n| Interaction-Aware Gaussian Weighting for Clustered Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dZAQxNFKGg)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fforum?id=XCLZgbm99O)] |\n| Efficient Heterogeneity-Aware Federated Active Data Selection |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pSdWTED0ZZ)] |\n| Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ny0m8YEUzH)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliaosunny123\u002FFedICU)] |\n| Rethinking the Temperature for Federated Heterogeneous Distillation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f9xsNQ8oSd)] |\n| FedClean: A General Robust Label Noise Correction for Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4kF2ZZcePc)] |\n| Federated Causal Structure Learning with Non-identical Variable Sets |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QlEx8f3S61)] |\n| FedECADO: A Dynamical System Model of Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gujuGnbhZr)] |\n| Efficient Federated Incomplete Multi-View Clustering |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sylDbssCU9)] [[CODE](https:\u002F\u002Fgithub.com\u002FTracesource\u002FEFIMVC)] |\n| Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7qvYLnJDRd)] [[CODE](https:\u002F\u002Fgithub.com\u002FPaddiHunter\u002FFIMCFG)] |\n| Local Pan-privacy for Federated Analytics |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M18dhHTFf8)] |\n| FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QwTDQXllam)] [[CODE](https:\u002F\u002Fgithub.com\u002FGanyuWang\u002FFedOne-BDPL)] |\n| Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zV5pkTMHPP)] [[CODE](https:\u002F\u002Fgithub.com\u002FHongyao-Chen\u002FHybridBN)] |\n| Private Federated Learning using Preference-Optimized Synthetic Data |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZuaU2bYzlc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmeiyuw\u002FPOPri)] |\n| Enhancing Foundation Models with Federated Domain Knowledge Infusion |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6SIVFmjIm4)] |\n| FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y7pDvbi9xz)] [[CODE](https:\u002F\u002Fgithub.com\u002FCYFang6\u002FFedPHA)] |\n| Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jwjvkWsePB)] [[CODE](https:\u002F\u002Fapp.box.com\u002Fs\u002Fphf6bhjy6owcr6b1rvfe412fiw059pxk)] |\n| Federated Node-Level Clustering Network with Cross-Subgraph Link Mending |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=38Nh0TebXZ)] |\n| Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mzPArjGqrs)] [[CODE](https:\u002F\u002Fgithub.com\u002Fallen4747\u002FFerret)] |\n| FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9hFQvmCl7P)] |\n| Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M7mVzCV6uU)] [[CODE](https:\u002F\u002Fgithub.com\u002FTerje-M\u002FFedGVI)] |\n| DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EvzArsKUww)] |\n| On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Eog0kXX7hW)] |\n| Safe-EF: Error Feedback for Non-smooth Constrained Optimization |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9D5aM5LQ3Y)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyardenas\u002Fsafe-ef)] |\n| Gradient Inversion of Multimodal Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j4IELrBhoG)] [[CODE](https:\u002F\u002Fgithub.com\u002FAlonZolfi\u002Fgi-dqa)] |\n| Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0p04srg7uf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkkhuge\u002FICML2025)] |\n| Decoupled SGDA for Games with Intermittent Strategy Communication |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZYkFTSEZ6k)] |\n| Private Model Personalization Revisited |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hw1kGPcSZ5)] |\n| Leveraging Randomness in Model and Data Partitioning for Privacy Amplification |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3K6BkFZ7ka)] |\n| Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XvOJvUlKc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpmangold\u002Fscaffold-speed-up)] |\n| Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hrBfufwMzg)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbuptcmm\u002Fphnhvvs)] |\n| FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V18WOxHRMq)] [[CODE](https:\u002F\u002Fgithub.com\u002Flxy66888\u002Ffedsmu.git)] |\n| One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=80mK2Mqaph)] [[CODE](https:\u002F\u002Fgithub.com\u002Fharrylee999\u002FFL-SAM)] |\n| Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dKfq3JbjnE)] |\n| Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XvF67vbCK)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzenghui9977\u002FFAFI_ICML25)] |\n| GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAk0ENu8LS)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiaruQian\u002FGHOST)] |\n| DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Nv6mOSqUVA)] |\n| BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fmlol78Qqf)] |\n| Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HdS6tZwwa7)] |\n| EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Bd9JlrqZhN)] [[CODE](https:\u002F\u002Fgithub.com\u002FZitongShi\u002FEAGLES)] |\n| Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Zn6hmmBnAa)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmahendrathapa\u002FpFedHB)] |\n| Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=R7gCixl2xR)] [[CODE](https:\u002F\u002Fgithub.com\u002FGivralNguyen\u002FFL-LDP-AMI)] |\n| Generalization in Federated Learning: A Conditional Mutual Information Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kOttDCDYJp)] |\n| The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aooq3tQIX9)] [[CODE](https:\u002F\u002Fgithub.com\u002FLeopold1423\u002Ffedmud-icml25)] |\n| Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rCJNbDXkvC)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdcll-iiitd\u002FCoresetForVFL)] |\n| Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ULZHqJU4ZC)] |\n| Federated In-Context Learning: Iterative Refinement for Improved Answer Quality |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TUk7gCqtmf)] |\n| SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kjz03pmyW0)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenddHe0119\u002FSPMC)] |\n| You Get What You Give: Reciprocally Fair Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZdmMDz33Io)] |\n| Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6znPjYn11w)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpupiu45\u002FFedGO)] |\n| Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iBOMvaa2aN)] |\n| HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06804-2)] |\n| Fedflow: a personalized federated learning framework for passenger flow prediction |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06795-0)] |\n| Federated causal inference from observational data |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06819-9)] |\n| TransFed: cross-domain feature alignment for semi-supervised federated transfer learning |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06805-1)] |\n| Improve global generalization for personalized federated learning within a Stackelberg game |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06770-9)] |\n| Efficient federated unlearning under plausible deniability |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-024-06685-x)] [[CODE](https:\u002F\u002Fgithub.com\u002FAyush-Umu\u002FFederated-Unlearning-under-Plausible-Deniability)] |\n| Energy-based Backdoor Defense Against Federated Graph Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5Jc7r5aqHJ)] |\n| DEPT: Decoupled Embeddings for Pre-training Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vf5aUZT0Fz)] |\n| Subgraph Federated Learning for Local Generalization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cH65nS5sOz)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsung-won-kim\u002FFedLoG)] |\n| Problem-Parameter-Free Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZuazHmXTns)] |\n| Adaptive Gradient Clipping for Robust Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=03OkC0LKDD)] |\n| Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cznqgb4DNv)] |\n| LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PpYy0dR3Qw)] |\n| Group Distributionally Robust Dataset Distillation with Risk Minimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3JsU5QXNru)] |\n| GRAIN: Exact Graph Reconstruction from Gradients |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7bAjVh3CG3)] |\n| Towards Faster Decentralized Stochastic Optimization with Communication Compression |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CMMpcs9prj)] |\n| Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Bl3e8HV9xW)] |\n| Many-Objective Multi-Solution Transport |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Neb17mimVH)] |\n| Query-based Knowledge Transfer for Heterogeneous Learning Environments |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XKv29sMyjF)] |\n| Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ydREOIttdC)] |\n| Federated Granger Causality Learning For Interdependent Clients With State Space Representation |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KTgQGXz5xj)] |\n| Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=omrLHFzC37)] |\n| Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TrJ36UfD9P)] |\n| On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7pDI74iOyu)] |\n| PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B9kUJuWrYC)] |\n| Differentially Private Federated Learning with Time-Adaptive Privacy Spending |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W0nydevOlG)] |\n| Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zPDpdk3V8L)] |\n| Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5DUekOKWcS)] |\n| On the Byzantine-Resilience of Distillation-Based Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=of6EuHT7de)] |\n| Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sYNWqQYJhz)] |\n| Event-Driven Online Vertical Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FCBbh0HCrF)] |\n| On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BfUDZGqCAu)] |\n| Federated Domain Generalization with Data-free On-server Matching Gradient |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8TERgu1Lb2)] |\n| Unlocking the Potential of Model Calibration in Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Osr0KZJeTX)] |\n| FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6RjQ54M1rM)] |\n| Understanding the Stability-based Generalization of Personalized Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=znhZbonEoe)] |\n| Federated Residual Low-Rank Adaption of Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=e0rQRMUhs7)] |\n| FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=44hcrfzydU)] |\n| Vertical Federated Learning with Missing Features During Training and Inference |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OXi1FmHGzz)] |\n| Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FoUpv84hMw)] |\n| Selective Aggregation for Low-Rank Adaptation in Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iX3uESGdsO)] |\n| Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Equ277PBN0)] |\n| Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B8akWa62Da)] |\n| Debiasing Federated Learning with Correlated Client Participation |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9h45qxXEx0)] |\n| Decoupled Subgraph Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=v1rFkElnIn)] |\n| Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=79nO2DPjVX)] |\n| Towards Federated RLHF with Aggregated Client Preference for LLMs |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mqNKiEB6pd)] |\n| SparsyFed: Sparse Adaptive Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OBUQNASaWw)] |\n| Can Textual Gradient Work in Federated Learning? |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Cy5IKvYbR3)] |\n| Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xiDJaTim3P)] |\n| Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3wEGdrV5Cb)] |\n| Connecting Federated ADMM to Bayes |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ipQrjRsl11)] |\n| Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ROpY0qRUXL)] |\n| Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f65RuQgVlp)] |\n| Federated Few-Shot Class-Incremental Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZiPoAlKf9Y)] |\n| Re-Fed+: A Better Replay Strategy for Federated Incremental Learning |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10930690)] |\n| DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10908045)] |\n| Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10833756)] |\n| Federated Multi-View K-Means Clustering |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10810504)] |\n| Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10696955)] |\n| Medical Federated Model With Mixture of Personalized and Shared Components |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10697408)] |\n| FedAST: Federated Asynchronous Simultaneous Training |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Faskin24a.html)] |\n| On Convergence of Federated Averaging Langevin Dynamics |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fdeng24a.html)] |\n| On the Convergence of Hierarchical Federated Learning with Partial Worker Participation |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fjiang24a.html)] |\n| Pure Exploration in Asynchronous Federated Bandits |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fwang24c.html)] |\n| One-shot Federated Learning via Synthetic Distiller-Distillate Communication |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6292sp7HiE)] |\n| Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uO53206oLJ)] |\n| FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=c3OZBJpN7M)] |\n| Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6lx34fpanw)] |\n| Federated Model Heterogeneous Matryoshka Representation Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5yboFMpvHf)] |\n| Federated Graph Learning for Cross-Domain Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=UBpPOqrBKE)] |\n| FedGMark: Certifiably Robust Watermarking for Federated Graph Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xeviQPXTMU)] |\n| Dual-Personalizing Adapter for Federated Foundation Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nkwPiBSw1f)] |\n| Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DUFD6vsyF8)] |\n| Taming the Long Tail in Human Mobility Prediction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wT2TIfHKp8)] |\n| Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EVw8Jh5Et9)] |\n| Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=55zLbH7dE1)] |\n| DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FDfrPugkGU)] |\n| Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DLNOBJa7TM)] |\n| Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FqWyzyErVT)] |\n| FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bMbteQRhDI)] |\n| Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nw6ANsC66G)] |\n| FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TcCorXxNJQ)] |\n| Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6SRPizFuaE)] |\n| pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xW6ga9i4eA)] |\n| Why Go Full? Elevating Federated Learning Through Partial Network Updates |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6OK8Qy9yVu)] |\n| FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E7fZOoiEKl)] |\n| FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I96GFYalFO)] |\n| Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3YIyB82rjX)] |\n| A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=h1iMVi2iEM)] |\n| Private and Personalized Frequency Estimation in a Federated Setting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0nzKznCjFG)] |\n| The Sample-Communication Complexity Trade-off in Federated Q-Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6YIpvnkjUK)] |\n| Federated Ensemble-Directed Offline Reinforcement Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ypaqE8UwsC)] |\n| Federated Black-Box Adaptation for Semantic Segmentation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Fp3JVz5XE7)] |\n| Thinking Forward: Memory-Efficient Federated Finetuning of Language Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dGQtja9X2C)] |\n| Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Y4L8GQXZZO)] |\n| Optimal Design for Human Preference Elicitation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cCGWj61Ael)] |\n| Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y6JotynERr)] |\n| Personalized Federated Learning via Feature Distribution Adaptation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wl2optQcng)] |\n| SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HeJ1cBAgiV)] |\n| A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hilGwNabqB)] |\n| RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=js74ZCddxG)] |\n| FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QXkFC7D6p4)] |\n| End-to-end Learnable Clustering for Intent Learning in Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=As91fJvY9E)] |\n| FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I3IuclVLFZ)] |\n| Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HS0faHRhWD)] |\n| FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D6MQrw9HFu)] |\n| A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3YkeHuT1o6)] |\n| FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zBMKodNgKX)] |\n| Low Precision Local Training is Enough for Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vvpewjtnvm)] |\n| Resource-Aware Federated Self-Supervised Learning with Global Class Representations |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Of4iNAIUSe)] |\n| On the Necessity of Collaboration for Online Model Selection with Decentralized Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uqWfLgZpV1)] |\n| The Power of Extrapolation in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FuTfZK7PK3)] |\n| (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lflwtGE6Vf)] |\n| On Sampling Strategies for Spectral Model Sharding |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PgTHgLUFi3)] |\n| Customizing Language Models with Instance-wise LoRA for Sequential Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=isZ8XRe3De)] |\n| SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dAXuir2ets)] |\n| HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6LVxO1C819)] |\n| Stabilized Proximal-Point Methods for Federated Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WukSyFSzDt)] |\n| DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Pezt0xttae)] |\n| Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g8wnC1E1OS)] |\n| Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j6Zsoj544N)] |\n| FedAvP: Augment Local Data via Shared Policy in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M1PRU0x1Iz)] |\n| CoBo: Collaborative Learning via Bilevel Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SjQ1iIqpfU)] |\n| Convergence Analysis of Split Federated Learning on Heterogeneous Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ud0RBkdBfE)] |\n| Communication-Efficient Federated Group Distributionally Robust Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xNZEjFe0mh)] |\n| Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YxyYTcv3hp)] |\n| Federated Learning over Connected Modes |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JL2eMCfDW8)] |\n| Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yvUHnBkCzd)] |\n| Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1kyc4TSOFZ)] |\n| Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=T826pwZLci)] |\n| DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=792txRlKit)] |\n| Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5FHzrRGOKR)] |\n| Hierarchical Federated Learning with Multi-Timescale Gradient Correction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aCAb1qNXI0)] |\n| HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3ie8NWA1El)] |\n| SPEAR: Exact Gradient Inversion of Batches in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lPDxPVS6ix)] |\n| Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WftaVkL6G2)] |\n| Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GVlJVX3iiq)] |\n| Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=G89r8Mgi5r)] |\n| Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0LfgE6kvKZ)] |\n| Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MwJo3zuiTm)] |\n| Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6ejpSVIiIl)] |\n| Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HhnpPISAUH)] |\n| FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JiRGxrqHh0)] |\n| Active preference learning for ordering items in- and out-of-sample |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PSLH5q7PFo)] |\n| Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gkOzoHBXUw)] |\n| Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WBLPlszJI5)] |\n| Revisiting Ensembling in One-Shot Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7rWTS2wuYX)] |\n| FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=djGx0hucok)] |\n| $\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I79q7wIRkS)] |\n| FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rovpCs3ZEO)] |\n| Momentum Approximation in Asynchronous Private Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pEpjKicxFk)] |\n| Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8TrYvsbw1f)] |\n| Federated Learning with Generative Content |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hMbgXHjWrg)] |\n| Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pxP2M3xiE6)] |\n| Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1JGa1OIRjQ)] |\n| Defection-Free Collaboration between Competitors in a Learning System |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2Sd5xNv1sm)] |\n| On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Eph8dS188u)] |\n| EncCluster: Bringing Functional Encryption in Federated Foundational Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7bgJ7t5kkW)] |\n| Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SXMsg44Znz)] |\n| Hot Pluggable Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FazIrAXoM6)] |\n| Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MxgmAil8ud)] |\n| The Future of Large Language Model Pre-training is Federated |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hfeH5AP9NY)] |\n| Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jNZEIQsJes)] |\n| The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RoUUV2wLdn)] |\n| ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zpEQUbYZPc)] |\n| DeComFL: Federated Learning with Dimension-Free Communication |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Vy9ltlTXXd)] |\n| Improving Group Connectivity for Generalization of Federated Deep Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vGyB8PVl4C)] |\n| MAP: Model Merging with Amortized Pareto Front Using Limited Computation |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KfOdVp4pfm)] |\n| OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qQdPSuW7qx)] |\n| Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OxpWu6J0TW)] |\n| Worldwide Federated Training of Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YMSLZUmQVV)] |\n| FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uBooD9HQQu)] |\n| Enhancing Causal Discovery in Federated Settings with Limited Local Samples |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Js64okXDUE)] |\n| $\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6WNNB9TaVw)] |\n| DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GdzTE7eruH)] |\n| FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization |  | JMLR | 2024 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv25\u002F23-0764.html)] |\n| Effective Federated Graph Matching |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rSfzchjIYu)] |\n| Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zwUEk9WpsR)] |\n| Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2zLt2Odckx)] |\n| FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AoYhtJ4A90)] |\n| Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=p0MGN0LSnx)] |\n| A New Theoretical Perspective on Data Heterogeneity in Federated Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=re6es2atbl)] |\n| Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models |  | ICML | 2024 | [[](https:\u002F\u002Fopenreview.net\u002Fforum?id=QgvBcOsF4B)] |\n| Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g43yUNWX4V)] |\n| Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Izv7gBnap3)] |\n| Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yHRxnhKyEJ)] |\n| Accelerating Federated Learning with Quick Distributed Mean Estimation |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gWEwIlZrbQ)] |\n| Fair Federated Learning via the Proportional Veto Core |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6Zgjrowepn)] |\n| AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PHUAG63Efe)] |\n| Recovering Labels from Local Updates in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E41gvBG4s6)] |\n| FedMBridge: Bridgeable Multimodal Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jrHUbftLd6)] |\n| Harmonizing Generalization and Personalization in Federated Prompt Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YYwERRXsJW)] |\n| Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6axTFAlzRV)] |\n| Accelerating Heterogeneous Federated Learning with Closed-form Classifiers |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cMige5MK1N)] |\n| Federated Combinatorial Multi-Agent Multi-Armed Bandits |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lrFwPeDdEQ)] |\n| A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GentO2E4ID)] |\n| Private Heterogeneous Federated Learning  Without a Trusted Server Revisited: Error-Optimal and  Communication-Efficient Algorithms for Convex Losses |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sSAEhcdB9N)] |\n| FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kc4dZYJlJG)] |\n| Pursuing Overall Welfare in Federated Learning through Sequential Decision Making |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=foPMkomvk1)] |\n| PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3WCvnkHnxV)] |\n| Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k2axqNsVVO)] |\n| Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mNzkumTSVL)] |\n| Federated Optimization with Doubly Regularized Drift Correction |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JD03zxWZzs)] |\n| FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0nMzOmkBHC)] |\n| Certifiably Byzantine-Robust Federated Conformal Prediction |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4axAQHwBOE)] |\n| Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vQmVmMN5ft)] |\n| Clustered Federated Learning via Gradient-based Partitioning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=J4HJUF70qm)] |\n| Recurrent Early Exits for Federated Learning with Heterogeneous Clients |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=w4B42sxNq3)] |\n| Rethinking the Flat Minima Searching in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6TM62kpI5c)] |\n| FedBAT: Communication-Efficient Federated Learning via Learnable Binarization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=x2zxPwCkAZ)] |\n| Federated Representation Learning in the Under-Parameterized Regime |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LIQYhV45D4)] |\n| FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=akyElNlUVA)] |\n| Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wuQ2DRPAuy)] |\n| SILVER: Single-loop variance reduction and application to federated learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pOgMluzEIH)] |\n| SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zEqeNEuiJr)] |\n| FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XecUTmB9yD)] |\n| Federated Continual Learning via Prompt-based Dual Knowledge Transfer |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kqa5JakTjB)] |\n| Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cit0hg4sEz)] |\n| Decomposable Submodular Maximization in Federated Setting |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SAbZExIIgG)] |\n| Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sTVSyqD6XX)] |\n| Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=01M0N8VgfB)] |\n| Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often! |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ffS0aYP6mk)] |\n| Byzantine Resilient and Fast Federated Few-Shot Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=q5q59s2WJy)] |\n| Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kbd9A4lVoX)] |\n| Ranking-based Client Imitation Selection for Efficient Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FMEhnS0948)] |\n| Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kVgpa1rfLO)] |\n| FADAS: Towards Federated Adaptive Asynchronous Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j56JAd29uH)] |\n| Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LIPGadocTe)] |\n| FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wjq2bS7fTK)] |\n| MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Jvh8HM9YEJ)] |\n| Federated Neuro-Symbolic Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EQXZqBXeW9)] |\n| Adaptive Group Personalization for Federated Mutual Transfer Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DqC9XiI71U)] |\n| Balancing Similarity and Complementarity for Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=v6tAdeCXKH)] |\n| Federated Self-Explaining GNNs with Anti-shortcut Augmentations |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZxDqSBgFSM)] |\n| A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NkN6wrYXe5)] |\n| COALA: A Practical and Vision-Centric Federated Learning Platform |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ATRnM8PyQX)] |\n| Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization |  | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-024-06541-y)] |\n| Communication-efficient clustered federated learning via model distance | USTC; State Key Laboratory of Cognitive Intelligence | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06443-5)] |\n| Federated learning with superquantile aggregation for heterogeneous data. | Google Research | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06332-x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.09429)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkrishnap25\u002Fsimplicial-fl)] |\n| Aligning model outputs for class imbalanced non-IID federated learning | NJU | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06241-5)] |\n| Federated Learning of Generalized Linear Causal Networks |  | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10480288)] |\n| Cross-Modal Federated Human Activity Recognition |  | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10440498)] |\n| Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling | Northeastern University; UoM | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10402074)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14008)] [[CODE](https:\u002F\u002Fgithub.com\u002FUMDataScienceLab\u002FFederated_Gaussian_Process)] |\n| The Impact of Adversarial Attacks on Federated Learning: A Survey | IIT | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10274102)] |\n| Understanding and Mitigating Dimensional Collapse in Federated Learning | NUS | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10336535)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00226)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbytedance\u002FFedDecorr)] |\n| No One Left Behind: Real-World Federated Class-Incremental Learning | CAS; UCAS | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10323204)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00903)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiahuaDong\u002FLGA)] |\n| Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning | WHU | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10295990)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.16286)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FFCCL)] |\n| Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy | HPU; XJTU | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10316599)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.07902)] [[CODE](https:\u002F\u002Fgithub.com\u002FIoTDATALab\u002FMAPA)] |\n| A Bayesian Federated Learning Framework With Online Laplace Approximation | SUSTech | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10274722)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01936)] [[CODE](https:\u002F\u002Fgithub.com\u002FKlitter\u002FA-Bayesian-Federated-Learning-Framework-with-Online-Laplace-Approximation)] |\n| Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting | USTC; HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tm8s3696Ox)] |\n| One-shot Empirical Privacy Estimation for Federated Learning | Google | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0BqyZSWfzo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03098)] |\n| Stochastic Controlled Averaging for Federated Learning with Communication Compression | LinkedIn; UPenn | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jj5ZjZsWJe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.08165)] |\n| A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging | IBM | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZKEuFKfCKA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03401)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002Ffedau)] |\n| A Mutual Information Perspective on Federated Contrastive Learning | QualComm | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JrmPG9ufKg)] |\n| Benchmarking Algorithms for Federated Domain Generalization | Purdue University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wprSv7ichW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.04942)] [[CODE](https:\u002F\u002Fgithub.com\u002Finouye-lab\u002FFedDG_Benchmark)] |\n| Effective and Efficient Federated Tree Learning on Hybrid Data | UC Berkeley | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=py4ZV2qYQI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11865)] |\n| Federated Recommendation with Additive Personalization | UTS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xkXdE81mOK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09109)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmtics\u002FFedRAP)] |\n| Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration | PSU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4aywmeb97I)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=4aywmeb97I&name=supplementary_material)] |\n| Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning | USC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAs4LdaP9Y)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nAs4LdaP9Y&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.01289)] |\n| Accurate Forgetting for Heterogeneous Federated Continual Learning | THU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ShQrnAsbPI)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FAF-FCL-7D65)] |\n| Federated Causal Discovery from Heterogeneous Data | MBZUAI | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=m7tJxajC3G)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.13241)] [[CODE](https:\u002F\u002Fgithub.com\u002Flokali\u002FFedCDH)] |\n| On Differentially Private Federated Linear Contextual Bandits | Wayne State University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cuAxSHcsSX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=cuAxSHcsSX&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.13945)] |\n| Incentivized Truthful Communication for Federated Bandits | University of Virginia | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ykEixGIJYb)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.04485)] |\n| Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting | UIUC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6J3ehSUrMU)] |\n| FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity | KAUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hbHwZYqk9T)] |\n| Text-driven Prompt Generation for Vision-Language Models in Federated Learning | Robert Bosch LLC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NW31gAylIm)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.06123)] |\n| Improving LoRA in Privacy-preserving Federated Learning | Northeastern University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NLPzL6HWNl)] |\n| FedWon: Triumphing Multi-domain Federated Learning Without Normalization | Sony AI | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hAYHmV1gM8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05879)] |\n| FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning | TU Delft | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DRu8PMHgCh)] |\n| FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler | ANL; UIUC; NCSA | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=msXxrttLOi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.14675)] [[CODE](https:\u002F\u002Fgithub.com\u002FAPPFL\u002FFedCompass)] [[PAGE](https:\u002F\u002Fappfl.github.io\u002FFedCompass)] |\n| Bayesian Coreset Optimization for Personalized Federated Learning | IIT Bombay | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uz7d2N2zul)] |\n| Layer-wise linear mode connectivity | Ruhr-Universtät Bochum | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LfmZh91tDI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.06966)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=LfmZh91tDI&name=supplementary_material)] |\n| Fake It Till Make It: Federated Learning with Consensus-Oriented Generation | SJTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NY3wMJuaLf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05966)] |\n| Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning | INSAIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=krx55l2A6G)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=krx55l2A6G&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03013)] |\n| Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning | Columbia University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D2eOVqPX9g)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.15273)] |\n| Adaptive Federated Learning with Auto-Tuned Clients | Rice University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g0mlwqs8pi)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=g0mlwqs8pi&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.11201)] |\n| Backdoor Federated Learning by Poisoning Backdoor-Critical Layers | ND | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AJBGSVSTT2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AJBGSVSTT2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.04466)] |\n| Federated Q-Learning: Linear Regret Speedup with Low Communication Cost | PSU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fe6ANBxcKM)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=fe6ANBxcKM&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.15023)] |\n| FedImpro: Measuring and Improving Client Update in Federated Learning | HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=giU9fYGTND)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.07011)] |\n| Federated Wasserstein Distance | MIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rsg1mvUahT)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rsg1mvUahT&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01973)] |\n| An improved analysis of per-sample and per-update clipping in federated learning | DTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BdPvGRvoBC)] |\n| FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation | NTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nbPGqeH3lt)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nbPGqeH3lt&name=supplementary_material)] |\n| Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning | HKU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Cc0qk6r4Nd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.11464)] |\n| Momentum Benefits Non-iid Federated Learning Simply and Provably | PKU; UPenn | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TdhkAcXkRi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.16504)] |\n| Communication-Efficient Federated Non-Linear Bandit Optimization | Yale University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nFI3wFM9yN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.01695)] |\n| Fair and Efficient Contribution Valuation for Vertical Federated Learning | Huawei | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sLQb8q0sUi)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=sLQb8q0sUi&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.02658)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenanf\u002FVerFedLogistic.jl)] |\n| Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SBj2Qdhgew)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11333)] |\n| Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients | PolyU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8FHWkY0SwF)] |\n| PeFLL: Personalized Federated Learning by Learning to Learn | IST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MrYiwlDRQO)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=MrYiwlDRQO&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05515)] |\n| Communication-Efficient Gradient  Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates | JHU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hORCalGn3Z)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=hORCalGn3Z&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05100)] |\n| FedInverse: Evaluating Privacy Leakage in Federated Learning | USQ | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nTNgkEIfeb)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nTNgkEIfeb&name=supplementary_material)] |\n| FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kjn99xFUF3)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=kjn99xFUF3&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06103)] |\n| Robust Training of Federated Models with Extremely Label Deficiency | HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qxLVaYbsSI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.14430)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvisitworld123\u002FTwin-sight)] |\n| Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory | RIKEN AIP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EcetCr4trp)] |\n| Teach LLMs to Phish: Stealing Private Information from Language Models | Princeton University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qo21ZlfNu6)] |\n| Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rM9VJPB20F)] |\n| Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise | HKUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CIqjp9yTDq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.14567)] |\n| Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks | CAS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=s8cMuxI5gu)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=s8cMuxI5gu&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03124)] [[CODE](https:\u002F\u002Fgithub.com\u002Fybwang119\u002Flabel_recovery)] |\n| Local Composite Saddle Point Optimization | Purdue University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kklwv4c4dI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15643)] |\n| Enhancing Neural Training via a Correlated Dynamics Model | TIIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=c9xsaASm9L)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13247)] |\n| EControl: Fast Distributed Optimization with Compression and Error Control | Saarland University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lsvlvWB9vz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=lsvlvWB9vz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05645)] |\n| Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit | HKUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=m52uU0dVbH)] |\n| FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kl9CqKf7h6)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Kl9CqKf7h6&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03156)] [[CODE](https:\u002F\u002Fgithub.com\u002FATP-1010\u002FFedHyper)] |\n| Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate | CUHK | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7pWRLDBAtc)] |\n| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | University of Cambridge | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zzqn5G9fjn)] |\n| Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1ii8idH4tH)] |\n| VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | THU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sqRgz88TM3)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09827)] [[CODE](https:\u002F\u002Fgithub.com\u002FFLAIR-THU\u002FVFLAIR)] |\n| Incentive-Aware Federated Learning with Training-Time Model Rewards | NUS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FlY7WQ2hWS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FlY7WQ2hWS&name=supplementary_material)] |\n| VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | NUS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=glwwbaeKm2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.02040)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FVertiBench)] |\n| FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | ZJU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V3j5d0GQgH)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=V3j5d0GQgH&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.08977)] |\n| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZdxGmJGKOo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19442)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ZdxGmJGKOo&name=supplementary_material)] |\n| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yKCLfOOIL7)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06351)] |\n| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=n3fPDW87is)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13591)] [[CODE](https:\u002F\u002Fgithub.com\u002FGeovaniRizk\u002FRobust-Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity)] |\n| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9OqezkNxnX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9OqezkNxnX&name=supplementary_material)] |\n| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Dxhv8Oja2V)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.03154)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliyipeng00\u002Fconvergence)] |\n| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LGKxz9clGG)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15165)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsarapieri\u002Ffed_het)] |\n| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance | JHU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rzDBoh1tBh)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rzDBoh1tBh&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09396)] |\n| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Rutgers University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=46x3zvYCyQ)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=46x3zvYCyQ&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13024)] |\n| Incentivized Communication for Federated Bandits | University of Virginia | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1aQivXgZKj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.11702)] |\n| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Northeastern University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M6UccKMFGl)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.12600)] |\n| IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Vanderbilt University; VinUniversity | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cemEOP8YoC)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=cemEOP8YoC&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsail-research\u002Fiba)] |\n| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning | KAIST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=P3Z59Okb5I)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=P3Z59Okb5I&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07485)] |\n| Federated Linear Bandits with Finite Adversarial Actions | University of Virginia | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bzXpQUnule)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=bzXpQUnule&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00973)] |\n| FedNAR: Federated Optimization with Normalized Annealing Regularization | MBZUAI | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=x5fs7TXKDc)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=x5fs7TXKDc&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03163)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljb121002\u002Ffednar)] |\n| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Concordia University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HRGd5dcVfw)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=HRGd5dcVfw&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03937)] [[CODE](https:\u002F\u002Fgithub.com\u002FGwenLegate\u002FGuidingLastLayerFLPretrain)] |\n| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | HZAU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0ycX03sMAT)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=0ycX03sMAT&name=supplementary_material)] |\n| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection | KAIST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2D7ou48q0E)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2D7ou48q0E&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17097)] [[CODE](https:\u002F\u002Fgithub.com\u002FKthyeon\u002Fssfod)] |\n| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | USC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3b9sqxCW1x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07784)] [[CODE](https:\u002F\u002Fgithub.com\u002FSaraBabakN\u002FMFCL-NeurIPS23)] |\n| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | UTS | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qJJmu4qsLO)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qJJmu4qsLO&name=supplementary_material)] |\n| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning | Rice University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KMxRQO7P98)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=KMxRQO7P98&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Flzcemma\u002FRACE_Distance)] |\n| Lockdown: Backdoor Defense for Federated Learning  with Isolated Subspace Training | Gatech | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V5cQH7JbGo)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=V5cQH7JbGo&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002FLockdown)] |\n| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | PSU; UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nX0zYBGEka)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nX0zYBGEka&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FFedGame)] |\n| Towards Personalized Federated Learning via Heterogeneous Model Reassembly | PSU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zpVCITHknd)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=zpVCITHknd&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.08643)] [[CODE](https:\u002F\u002Fgithub.com\u002FJackqqWang\u002FpfedHR)] |\n| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction | GWU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AWpWaub6nf)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AWpWaub6nf&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08670)] |\n| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | ECNU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3H9QH1v6U9)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3H9QH1v6U9&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13546)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FDFRD-0C83\u002F)] |\n| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning | Western University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AYiRHZirD2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AYiRHZirD2&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FGanyuWang\u002FVFL-CZOFO)] |\n| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Xidian University; University of Guelph; Zhejiang Key Laboratory of Multi-dimensional Perception Technology, Application and Cybersecurity | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3n8PNUdvSg)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3n8PNUdvSg&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.05431)] |\n| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | SJTU; Shanghai AI Laboratory | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wwmKVO8bsR)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=wwmKVO8bsR&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedGELA)] |\n| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | GMU; SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Yq6GKgN3RC)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Yq6GKgN3RC&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002Fepisode_plusplus)] |\n| FedL2P: Federated Learning to Personalize | University of Cambridge; Samsung AI Center | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FM81CI68Iz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FM81CI68Iz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02420)] [[CODE](https:\u002F\u002Fgithub.com\u002Froyson\u002Ffedl2p\u002F)] |\n| Adaptive Test-Time Personalization for Federated Learning | UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rbw9xCU6Ci)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18816)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002FATP)] |\n| Federated Conditional Stochastic Optimization | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E0Gw1uz7lU)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=E0Gw1uz7lU&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02524)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FFederated-Minimax-and-Conditional-Stochastic-Optimization\u002Ftree\u002Fmain)] |\n| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RW7rZ8Y3Bp)] |\n| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EcmqyXekuP)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=EcmqyXekuP&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12534)] |\n| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ody3RBUuJS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ody3RBUuJS&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12433)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyh-yao\u002FFedGCN)] |\n| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OlSTwlz96r)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=OlSTwlz96r&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09866)] |\n| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rG1M3kOVba)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rG1M3kOVba&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.02623)] [[CODE](https:\u002F\u002Fgithub.com\u002Fiwang05\u002FFLuID)] |\n| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j4QVhftpYM)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=j4QVhftpYM&name=supplementary_material)] |\n| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B5XwENgy0T)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=B5XwENgy0T&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06701)] |\n| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IjZa2fQ8tL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00227)] |\n| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | The University of Sydney | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ylPX5D7It7)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ylPX5D7It7&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05706)] |\n| DELTA: Diverse Client Sampling for Fasting Federated Learning | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6XC5iKqRVm)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=6XC5iKqRVm&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13925)] |\n| Federated Compositional Deep AUC Maximization | Temple University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tF7W8ai8J3)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=tF7W8ai8J3&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10101)] |\n| A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning | PSU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=S6ajVZy6FA)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=S6ajVZy6FA&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhfzhang31\u002FA3FL)] |\n| Flow: Per-instance Personalized Federated Learning | University of Massachusetts | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BI031mw7iS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=BI031mw7iS&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.15281)] [[CODE](https:\u002F\u002Fgithub.com\u002FAstuary\u002FFlow)] |\n| Eliminating Domain Bias for Federated Learning in Representation Space | SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nO5i1XdUS0)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nO5i1XdUS0&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.14975)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FDBE)] |\n| Federated Learning with Manifold Regularization and Normalized Update Reaggregation | BIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7uPnuoYqac)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7uPnuoYqac&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05924)] |\n| Structured Federated Learning through Clustered Additive Modeling | University of Technology Sydney | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XT3UpOv48)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2XT3UpOv48&name=supplementary_material)] |\n| Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | ZJU; Singapore University of Technology and Design | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gJewjFjfN2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gJewjFjfN2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07587)] [[CODE](https:\u002F\u002Fgithub.com\u002FZackZikaiXiao\u002FFedGraB)] |\n| Dynamic Personalized Federated Learning with Adaptive Differential Privacy | WHU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RteNLuc8D9)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=RteNLuc8D9&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxiyuanyang45\u002FDynamicPFL)] |\n| Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning | ShanghaiTech University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dEDdRWunxU)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=dEDdRWunxU&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13923)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhyczy\u002FFed-CO2)] |\n| Solving a Class of Non-Convex  Minimax Optimization in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SpStmVboGy)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SpStmVboGy&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03613)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FFederated-Minimax-and-Conditional-Stochastic-Optimization\u002F)] |\n| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VNyKBipt91)] [[CODE](https:\u002F\u002Fgithub.com\u002Finsujeon\u002FMetaVD)] |\n| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0ORqsMY6OL)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=0ORqsMY6OL&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19807)] |\n| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | NTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tmxjuIFSEc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fculiver\u002FSPACE)] |\n| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | PKU; Tencent | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=txPdKZrrZF)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=txPdKZrrZF&name=supplementary_material)] |\n| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | BUAA; HKBU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=phnGilhPH8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.05077)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvisitworld123\u002Ffedfed)] |\n| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning | SCU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kuxu4lCRr5)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=kuxu4lCRr5&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09183)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbdemo\u002Fpfedbred_public)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F661506638)] |\n| Spectral Co-Distillation for Personalized Federated Learning | SUTD | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RqjQL08UFc)] |\n| Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy | ZJU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4ZaPpVDjGQ)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=4ZaPpVDjGQ&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09624)] |\n| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7ETbK9lQd7)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7ETbK9lQd7&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04924)] [[CODE](https:\u002F\u002Fgithub.com\u002FBerivanIsik\u002Frrsc)] |\n| (Amplified) Banded Matrix Factorization: A unified approach to private training | Google DeepMind | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zEm6hF97Pz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=zEm6hF97Pz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.08153)] |\n| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | KIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nXNsqB4Yr1)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nXNsqB4Yr1&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17005)] [[CODE](https:\u002F\u002Fgithub.com\u002Fk1l1\u002FSLT)] |\n| Privacy Amplification via Compression:  Achieving the Optimal Privacy-Accuracy-Communication Trade-off in  Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=izNfcaHJk0)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=izNfcaHJk0&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01541)] |\n| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zurich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g2ROKOASiv)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=g2ROKOASiv&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16272)] |\n| Resilient Constrained Learning | UPenn | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=h0RVoZuUl6)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=h0RVoZuUl6&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02426)] |\n| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | KAUST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=loxinzXlCx)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=loxinzXlCx&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15580)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmysteryresearcher\u002Fdasha-partial-participation)] |\n| Collaboratively Learning Linear Models with Structured Missing Data | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=waDF0oACu2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=waDF0oACu2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11947)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryxcheng\u002Fcollab)] |\n| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | EPFL | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qCglMj6A4z)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qCglMj6A4z&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.01463)] |\n| Fast Optimal Locally Private Mean Estimation via Random Projections | Apple Inc. | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=K3JgUvDSYX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=K3JgUvDSYX&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04444)] [[CODE](https:\u002F\u002Fgithub.com\u002Fapple\u002Fml-projunit)] |\n| Contextual Stochastic Bilevel Optimization | EPFL; ETH Zürich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SHBksHKutP)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SHBksHKutP&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18535)] |\n| Understanding Deep Gradient Leakage via Inversion Influence Functions | MSU; Michigan State University; University of Texas at Austin | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tBib2fWr3r)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=tBib2fWr3r&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13016)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002Finversion-influence-function)] |\n| Inner Product-based Neural Network Similarity | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9eneYFIGKq)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9eneYFIGKq&name=supplementary_material)] |\n| Correlation Aware Sparsified Mean Estimation Using Random Projection | CMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VacSQpbI0U)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=VacSQpbI0U&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18868)] [[CODE](https:\u002F\u002Fgithub.com\u002F11hifish\u002FRand-Proj-Spatial)] |\n| TIES-Merging: Resolving Interference When Merging Models | UNC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xtaX3WyCj1)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=xtaX3WyCj1&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.01708)] [[CODE](https:\u002F\u002Fgithub.com\u002Fprateeky2806\u002Fties-merging)] |\n| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qyixBZl8Ph)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qyixBZl8Ph&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.04792)] [[CODE](https:\u002F\u002Fgithub.com\u002Faparna-aketi\u002Fglobal_update_tracking)] |\n| Large-Scale Distributed Learning via Private On-Device LSH | UMD | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dpdbbN7AKr)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=dpdbbN7AKr&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02563)] |\n| Faster Relative Entropy Coding with Greedy Rejection Coding | University of Cambridge | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KXbAgvLi2l)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=KXbAgvLi2l&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.15746)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcambridge-mlg\u002Ffast-rec-with-grc)] |\n| Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization | SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gVLKXT9JwG)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gVLKXT9JwG&name=supplementary_material)] |\n| Momentum Provably Improves Error Feedback! | ETH AI Center; ETH Zurich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1h92PmnKov)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=1h92PmnKov&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15155)] |\n| Strategic Data Sharing between Competitors | Sofia University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AkK3S2spZs)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AkK3S2spZs&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16052)] |\n| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets | GMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M4h1UAxI3b)] |\n| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | Wyze Labs | NeurIPS Datasets and Benchmarks | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qynH28Y4xE)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qynH28Y4xE&name=supplementary_material)] [[DATASET](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fwyzelabs\u002FRuleRecommendation)] |\n| Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning | Google Research | NeurIPS Datasets and Benchmarks | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EPz1DcdPVE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09619)] [[DATASET](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fdataset_grouper)] |\n| Text-driven Prompt Generation for Vision-Language Models in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8zduZGpzZl)] |\n| HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dsWg7n6zoo)] |\n| Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=H0inHCV05c)] |\n| FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XJhL1XlefX)] |\n| FedSoL: Bridging Global Alignment and Local Generality in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WYLhRgBAFH)] |\n| One-shot Empirical Privacy Estimation for Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JmrHzzDiyI)] |\n| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5JsO2DClwk)] |\n| SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=06quMTmtRV)] |\n| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xqvB784PCv)] |\n| Towards Building the FederatedGPT: Federated Instruction Tuning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TaDiklyVps)] |\n| Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ozN92d7CHX)] |\n| LASER: Linear Compression in Wireless Distributed Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PmahoyE89G)] |\n| MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YqqWQP8POe)] |\n| TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SvJx4a75QZ)] |\n| An Empirical Evaluation of Federated Contextual Bandit Algorithms |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qwnOt7FFSD)] |\n| RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FakNykU4PF)] |\n| FDAPT: Federated Domain-adaptive Pre-training for Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ESCL5T3EgV)] |\n| Making Batch Normalization Great in Federated Deep Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iKQC652XIk)] |\n| Correlated Noise Provably Beats Independent Noise for Differentially Private Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AbrnDOw8R9)] |\n| Parameter Averaging Laws for Multitask Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qQ2qXFu05s)] |\n| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HyRwexERAo)] |\n| Beyond Parameter Averaging in Model Aggregation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sPtEDSVD4K)] |\n| Augmenting Federated Learning with Pretrained Transformers |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ldN6QdyukS)] |\n| Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=le0Emy9SqA)] |\n| DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=s7hquGszME)] |\n| Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gACRiXPGmM)] |\n| FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PuYD0fh5aq)] |\n| Learning Optimizers for Local SGD |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HiPe4SjZMs)] |\n| Exploring User-level Gradient Inversion with a Diffusion Prior |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lcElZPvMFp)] |\n| User Inference Attacks on Large Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4uyyLG4KCH)] |\n| FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1ww9tjEQVL)] |\n| Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EmV9sGpZ7q)] |\n| Backdoor Threats from Compromised Foundation Models to Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BrcHuO2BVc)] |\n| MOFL\u002FD: A Federated Multi-objective Learning Framework with Decomposition |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Pj6BPHZy56)] |\n| Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OoEIUohfcp)] |\n| Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LiSj1GRVhL)] |\n| FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4apX9Kcxie)] |\n| Private and Personalized Histogram Estimation in a Federated Setting |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XSfsvBoc8M)] |\n| The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | COLT | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002Fzhao23b.html)] |\n| FLASH: Automating federated learning using CASH | Rensselaer Polytechnic Institute | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5L66DZpPSHk)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Falam23a\u002Falam23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=5L66DZpPSHk&name=other_supplementary_material)] |\n| Personalized federated domain adaptation for item-to-item recommendation | AWS AI Labs | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7ypu4_en3Zm)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03191)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Ffan23a\u002Ffan23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7ypu4_en3Zm&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzfan20\u002FPFGNNPlus)] |\n| Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning | Baidu Research | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Q06wKxnHRv)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00532)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fkarimi23a\u002Fkarimi23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Q06wKxnHRv&name=other_supplementary_material)] |\n| Federated learning of models pre-trained on different features with consensus graphs | IBM Research | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gSMiXJmMEOf)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fma23b\u002Fma23b-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gSMiXJmMEOf&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmatenure\u002Ffederated_feature_fusion)] |\n| Fast Heterogeneous Federated Learning with Hybrid Client Selection | NWPU | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JtSlA972EHP)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fsong23b\u002Fsong23b-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=JtSlA972EHP&name=other_supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.05135)] |\n| Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning | Cornell University | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Gt_GiNkBhu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10880)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fwu23a\u002Fwu23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Gt_GiNkBhu&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwrh14\u002Flearning_to_invert)] |\n| Dynamic Regularized Sharpness Aware Minimization in Federated Learning:  Approaching Global Consistency and Smooth Landscape | The University of Sydney | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vD1R00hROK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.11584)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F24651.pdf)] |\n| Analysis of Error Feedback in Federated  Non-Convex Optimization with Biased Compression: Fast Convergence and  Partial Participation | LinkedIn Ads | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wbs1fKLfOe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14292)] |\n| FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization | Alibaba Group | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=891ytYlYgB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03966)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Fmaster\u002Fbenchmark\u002FFedHPOBench)] |\n| Federated Conformal Predictors for Distributed Uncertainty Quantification | MIT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YVTr9PzIrK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17564)] [[CODE](https:\u002F\u002Fgithub.com\u002Fclu5\u002Ffederated-conformal)] |\n| Federated Adversarial Learning: A Framework with Convergence Analysis | UBC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kgvoV2KcTJ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.03635)] |\n| Federated Heavy Hitter Recovery under Linear Sketching | Google Research | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zN4oRCrlnM)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.13347)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated)] |\n| Doubly Adversarial Federated Bandits | London School of Economics and Political Science | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FjOB0g7iRf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09223)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjialinyi94\u002Fdoubly-stochastic-federataed-bandit)] |\n| Achieving Linear Speedup in Non-IID Federated Bilevel Learning | UC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XFpTtAWNpQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.05412)] |\n| One-Shot Federated Conformal Prediction | Université Paris-Saclay | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SZJGIWe1Ag)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06322)] [[CODE](https:\u002F\u002Fgithub.com\u002FpierreHmbt\u002FFedCP-QQ)] |\n| Federated Online and Bandit Convex Optimization | TTIC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mi7pnouqLa)] |\n| Federated Linear Contextual Bandits with User-level Differential Privacy | The Pennsylvania State University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=b9opfVNw6O)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05275)] |\n| Vertical Federated Graph Neural Network for Recommender System | NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NRnS6CtbaN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.05786)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmaiph123\u002Fverticalgnn)] |\n| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | University at Buffalo | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IYyhNudD9V)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04969)] |\n| Towards Understanding Ensemble Distillation in Federated Learning | KAIST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xx0TH4IKgQ)] |\n| Personalized Subgraph Federated Learning | KAIST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GXHL8ZS1GX)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10206)] [[CODE](https:\u002F\u002Fgithub.com\u002FJinheonBaek\u002FFED-PUB)] |\n| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift | Lagrange Mathematics and Computing Research Center; CMAP | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ytpEqHYSEy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05131)] |\n| Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ICk7GJ1awE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14618)] |\n| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JC05k0E2EM)] [[CODE](https:\u002F\u002Fgithub.com\u002FYamingGuo98\u002FFedIIR)] |\n| Personalized Federated Learning under Mixture of Distributions | UCLA | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nmVOTsQGR9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.01068)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzshuai8\u002FFedGMM_ICML2023)] |\n| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cHJ1VuZorx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19229)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedDisco)] |\n| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ht9r3P6Lts)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05891)] [[CODE](https:\u002F\u002Fgithub.com\u002Fharliwu\u002Ffedamd)] |\n| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y8qAZhWbNs)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10999)] |\n| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Auburn University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6wQKmKiDHw)] |\n| On the Convergence of Federated Averaging with Cyclic Client Participation | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=d8LTNXt97w)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03109)] |\n| Revisiting Weighted Aggregation in Federated Learning with Neural Networks | ZJU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FuDAjnWhrQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10911)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzexilee\u002Ficml-2023-fedlaw)] |\n| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WfI3I8OjHS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10697)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F24679_ljO6pDE.pdf)] |\n| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning | UESTC; NTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iASUTBGw07)] |\n| Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=q5RHsg6VRw)] |\n| DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VxKr51JjWC)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00543)] |\n| FeDXL: Provable Federated Learning for Deep X-Risk Optimization | Texas A&M University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=C7fNCYdptO)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14396)] [[CODE](https:\u002F\u002Fgithub.com\u002Foptimization-ai\u002Ficml2023_fedxl)] |\n| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | HIT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AMuNQEUmGr)] [[CODE](https:\u002F\u002Fgithub.com\u002FHypervoyager\u002FPFL)] |\n| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=33fj5Ph3ot)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FpFedGraph)] |\n| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rnNBSMOWvA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06508)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002Ffedcollab)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F23569.pdf)] |\n| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zurich | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mRiDy4qGwB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01785)] [[CODE](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Ftableak)] |\n| FedCR:  Personalized Federated Learning Based on Across-Client Common  Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YDC5jTS3LR)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaozzh\u002FFedCR)] |\n| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Duke University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NcbY2UOfko)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.15245)] |\n| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta AI | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Otdp5SGQMr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.03942)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fdp_compression)] |\n| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning | Owkin Inc. | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pRsJIVcjxD)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.07644)] [[CODE](https:\u002F\u002Fgithub.com\u002Fowkin\u002Fsratta)] |\n| Improving the Model Consistency of Decentralized Federated Learning | THU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fn2NFlYLBL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04083)] |\n| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Alibaba Group | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ieSN7Xyo8g)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.02776)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyxdyc\u002Fpfedgate)] |\n| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CBLDv6SFMn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12559)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftotilas\u002Fpadadmm)] |\n| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2CiaH2Tq4G)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchaoyitud\u002FLeadFL)] |\n| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HtHFnHrZXu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12961)] [[CODE](https:\u002F\u002Fgithub.com\u002Fybdai7\u002Fchameleon-durable-backdoor)] |\n| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7aqVcrXjxa)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13407)] |\n| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nDKoVwNjMH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13462)] [[CODE](https:\u002F\u002Fgithub.com\u002Flins-lab\u002Ffedbr)] |\n| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gHfybro5Sj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.00771)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F25109.pdf)] |\n| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta AI | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ai1TyAjZt9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05578)] |\n| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Leuven | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kz0IODB2kj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.00127)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjunyizhu-ai\u002Fsurrogate_model_extension)] |\n| Fair yet Asymptotically Equal Collaborative Learning | NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5VhltFPSO8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05764)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxqlin98\u002FFair-yet-Equal-CML)] |\n| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uIzkbJgyqc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08371)] |\n| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DVF7gEQQf7)] |\n| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uGORNDmIdr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06090)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbingzhaozhu\u002Fxtab)] |\n| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=a0kGwNUwil)] |\n| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3DI6Kmw81p)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06079)] [[CODE](https:\u002F\u002Fgithub.com\u002FYuchenLiu-a\u002Fbyzantine-gas)] |\n| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Rensselaer Polytechnic Institute | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=L8iWCxzwl1)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.02219)] |\n| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eqTWOzheZT)] |\n| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | University of Chicago | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iAgQfF3atY)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02543)] [[CODE](https:\u002F\u002Fgithub.com\u002Fboxinz17\u002Fdata-market-via-adaptive-sampling)] |\n| Ensemble and continual federated learning for classification tasks. | Universidade de Santiago de Compostela | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06330-z)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07129)] |\n| FAC-fed: Federated adaptation for fairness and concept drift aware stream classification | Leibniz University of Hannover | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06360-7)] |\n| Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06292-8)] |\n| FedLab: A Flexible Federated Learning Framework :fire: | UESTC; Peng Cheng Lab | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0440.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.11621)] [[CODE](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedLab)] |\n| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? |  | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-0224.html)] |\n| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-1301.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04911)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbokun-wang\u002Fmoml)] |\n| A First Look into the Carbon Footprint of Federated Learning | University of Cambridge | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-0445.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07627)] |\n| Attacks against Federated Learning Defense Systems and their Mitigation | The University of Newcastle | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0014.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcodymlewis\u002Fviceroy)] |\n| A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates | Universit ́e Cˆ ote d’Azur | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0689.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10189)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning\u002Ftree\u002Fasynchronous_FL)] |\n| Tighter Regret Analysis and Optimization of Online Federated Learning | Hanyang University | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10255290)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.06491)] |\n| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.00522)] |\n| Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10040221)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10607)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenglongZhou\u002FFedADMM)] |\n| FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9847383)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.13236)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpurp1eHaze\u002FFedIPR)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F562837170)] |\n| Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9850408)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.11375)] |\n| Personalized Federated Learning with Feature Alignment and Classifier Collaboration | THU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SXZr8aDKia)] [[CODE](https:\u002F\u002Fgithub.com\u002FJianXu95\u002FFedPAC)] |\n| MocoSFL: enabling cross-client collaborative self-supervised learning | ASU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2QGJXyMNoPz)] [[CODE](https:\u002F\u002Fgithub.com\u002FSonyAI\u002FMocoSFL)] |\n| Single-shot General Hyper-parameter Optimization for Federated Learning | IBM | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3RhuF8foyPW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08338)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3RhuF8foyPW&name=SUPP_material)] |\n| Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated | Facebook | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Mpa3tRJFBb)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15387)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwhere_to_begin)] |\n| FedExP: Speeding up Federated Averaging via Extrapolation | CMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IPrzNbddXV)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09604)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdivyansh03\u002Ffedexp)] |\n| Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection | MSU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mMNimwRb7Gr)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FFOSTER)] |\n| DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity | KAUST | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VA1YpcNr7ul)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.01268)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmysteryresearcher\u002Fdasha)] |\n| Machine Unlearning of Federated Clusters | University of Illinois | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VzwfoFyYDga)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.16424)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=VzwfoFyYDga&name=SUPP_material)] |\n| Federated Neural Bandits | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=38m4h8HcNRL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14309)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=38m4h8HcNRL&name=SUPP_material)] |\n| FedFA:  Federated Feature Augmentation | ETH Zurich | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=U9yFP90jU0)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12995)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftfzhou\u002Ffedfa)] |\n| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach | CMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dZrQR7OR11)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04228)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhanguo97\u002Fexpectation-propagation)] |\n| Better Generative Replay for Continual Federated Learning | University of Virginia | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cRxYWKiTan)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaiqing98\u002FFedCIL)] |\n| Federated Learning from Small Datasets | IKIM | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hDDV1lsRV8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03469)] |\n| Federated Nearest Neighbor Machine Translation | USTC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=R1U5G2spbLd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12211)] |\n| Meta Knowledge Condensation for Federated Learning | A*STAR | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TDf-XFAwc79)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.14851)] |\n| Test-Time Robust Personalization for Federated Learning | EPFL | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3aBuJEza5sq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.10920)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3aBuJEza5sq&name=SUPP_material)] |\n| DepthFL : Depthwise Federated Learning for Heterogeneous Clients | SNU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pf8RIZTMU58)] |\n| Towards Addressing Label Skews in One-Shot Federated Learning | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rzrqh85f4Sc)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rzrqh85f4Sc&name=SUPP_material)] |\n| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EXnIyMVTL8s)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00226)] [[CODE](https:\u002F\u002Fgithub.com\u002FYujun-Shi\u002FFedCLS)] |\n| Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=A9WQaxYsfx)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=A9WQaxYsfx&name=SUPP_material)] |\n| SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jh1nCir1R3d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14026)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=jh1nCir1R3d&name=SUPP_material)] |\n| Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses | USC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TVY6GoURrw)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09779)] [[CODE](https:\u002F\u002Fgithub.com\u002Flowya\u002Fprivate-federated-learning-without-a-trusted-server)] |\n| Effective passive membership inference attacks in federated learning against overparameterized models | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QsCSLPP55Ku)] |\n| FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification | University of Cambridge | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9aokcgBVIj1)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08671)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9aokcgBVIj1&name=SUPP_material)] |\n| Multimodal Federated Learning via Contrastive Representation Ensemble | THU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Hnk1WRMAYqg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.08888)] [[CODE](https:\u002F\u002Fgithub.com\u002Fflair-thu\u002Fcreamfl)] |\n| Faster federated optimization under second-order similarity | Princeton University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ElC6LYO4MfD)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02257)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ElC6LYO4MfD&name=SUPP_material)] |\n| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | University of Sydney | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bZjxxYURKT)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=bZjxxYURKT&name=SUPP_material)] |\n| The Best of Both Worlds: Accurate  Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | utexas | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=29V3AWjVAFi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08968)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=29V3AWjVAFi&name=SUPP_material)] |\n| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | UBC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hxEIgUXLFF)] [[CODE](https:\u002F\u002Fgithub.com\u002FMehdiSet\u002FPerFedMask)] |\n| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data | GMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ytZIYmztET)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002Fepisode)] |\n| FedDAR: Federated Domain-Aware Representation Learning | Harvard | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6P9Y25Pljl6)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04007)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlz0414\u002FFedDAR)] |\n| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning | upenn | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oJpVVGXu9i)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FCENTAUR-Privacy-Federated-Representation-Learning)] |\n| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xo2E217_M4n)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.12873)] [[CODE](https:\u002F\u002Fgithub.com\u002FKaiyuanZh\u002FFLIP)] |\n| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses | RUC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=-EHqoysUYLx)] |\n| Efficient Federated Domain Translation | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uhLAcrAZ9cJ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=uhLAcrAZ9cJ&name=SUPP_material)] |\n| On the Importance and Applicability of Pre-Training for Federated Learning | OSU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fWWFv--P0xP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.11488)] [[CODE](https:\u002F\u002Fgithub.com\u002Fandytu28\u002Ffps_pre-training)] |\n| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=r0BrY4BiEXO)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12675)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy | UCLA | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FUiDMCr_W4o)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.01771)] |\n| Instance-wise Batch Label Restoration via Gradients in Federated Learning | BUAA | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FIrQfNSOoTr)] [[CODE](https:\u002F\u002Fgithub.com\u002FBUAA-CST\u002FiLRG)] |\n| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity | College of William and Mary | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_hb4vM3jspB)] |\n| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning | University of Warwick | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kf7Yyf4O0u)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02912)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fcanife)] |\n| Sparse Random Networks for Communication-Efficient Federated Learning | Stanford | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k1FHgri5y3-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.15328)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=k1FHgri5y3-&name=SUPP_material)] |\n| Combating Exacerbated Heterogeneity for Robust Decentralized Models | HKBU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eKllxpLOOm)] [[CODE](https:\u002F\u002Fgithub.com\u002FZFancy\u002FSFAT)] |\n| Hyperparameter Optimization through Neural Network Partitioning | University of Cambridge | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAgdXgfmqj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.14766)] |\n| Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? | MIT | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2L9gzS80tA4)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10947)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2L9gzS80tA4&name=SUPP_material)] |\n| Variance Reduction is an Antidote  to Byzantines: Better Rates, Weaker Assumptions and Communication  Compression as a Cherry on the Top | mbzuai | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pfuqQQCB34)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00529)] [[CODE](https:\u002F\u002Fgithub.com\u002FSamuelHorvath\u002FVR_Byzantine)] |\n| Dual Diffusion Implicit Bridges for Image-to-Image Translation | Stanford | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5HLoTvVGDe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08382)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=5HLoTvVGDe&name=SUPP_material)] |\n| An accurate, scalable and verifiable protocol for federated differentially private averaging | INRIA Lille | Mach Learn | 2022 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06267-9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07218)] |\n| Federated online clustering of bandits. | CUHK | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rKUgiU8iqeq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.14865)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhaohaoru\u002Ffederated-clustering-of-bandits)] |\n| Privacy-aware compression for federated data analysis. | Meta AI | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BqUdRP8i9e9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08134)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fdp_compression)] |\n| Faster non-convex federated learning via global and local momentum. | UTEXAS | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SSlLRUIs9e9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.04061)] |\n| Fedvarp: Tackling the variance due to partial client participation in federated learning. | CMU | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HlWLLdUocx5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.14130)] |\n| SASH: Efficient secure aggregation based on SHPRG for federated learning | CAS; CASTEST | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HSleBPIoql9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.12321)] |\n| Bayesian federated estimation of causal effects from observational data | NUS | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BEl3vP8sqlc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00456)] |\n| Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Hanyang University | TPAMI | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9625795)] |\n| Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning | ZJU | TPAMI | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9238427)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsunjunaimer\u002FTPAMI-LAQ)] |\n| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox | Moscow Institute of Physics and Technology | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W72rB0wwLVu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03957)] |\n| LAMP: Extracting Text from Gradients with Language Model Priors | ETHZ | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6iqd9JAVR1z)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=6iqd9JAVR1z&name=SUPP_material)] |\n| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning | utexas | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=G3fswMh9P8y)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13692)] |\n| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond | NUIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_33ynl9VgCX)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05187)] |\n| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | WISC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=i9XrHJoyLqJ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=i9XrHJoyLqJ&name=SUPP_material)] |\n| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | Columbia University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Vj-jYs47cx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10870)] |\n| Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective | PKU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wo-a8Ji6s3A)] |\n| Subspace Recovery from Heterogeneous Data with Non-isotropic Noise | Stanford | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mUeMOdJ2IJp)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.13497)] |\n| EF-BV:  A Unified Theory of Error Feedback and Variance Reduction Mechanisms  for Biased and Unbiased Compression in Distributed Optimization | KAUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PeJO709WUup)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.04180)] |\n| On-Demand Sampling: Learning Optimally from Multiple Distributions | UC Berkeley | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FR289LMkmxZ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FR289LMkmxZ&name=SUPP_material)] |\n| Improved Utility Analysis of Private CountSketch | ITU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XFCirHGr4Cs)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.08397)] [[CODE](https:\u002F\u002Fgithub.com\u002Frasmus-pagh\u002Fprivate-countsketch)] |\n| Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | HUAWEI | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=APXedc0hgdT)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=APXedc0hgdT&name=SUPP_material)] |\n| Decentralized Local Stochastic Extra-Gradient for Variational Inequalities | phystech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Y4vT7m4e3d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08315)] |\n| BEER: Fast O(1\u002FT) Rate for Decentralized Nonconvex Optimization with Communication Compression | Princeton | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I47eFCKa1f3)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.13320)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliboyue\u002Fbeer)] |\n| Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning | The University of Tokyo | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KOHC_CYEIuP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.06083)] |\n| Near-Optimal Collaborative Learning in Bandits | INRIA; Inserm | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2xfJ26BuFP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00121)] [[CODE](https:\u002F\u002Fgithub.com\u002Fclreda\u002Fnear-optimal-federated)] |\n| Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | phystech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=J0nhRuMkdGf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03313)] |\n| Towards Optimal Communication Complexity in Distributed Non-Convex Optimization | TTIC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SNElc7QmMDe)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SNElc7QmMDe&name=SUPP_material)] |\n| FedPop: A Bayesian Approach for Personalised Federated Learning | Skoltech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KETwimTQexH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03611)] |\n| Fairness in Federated Learning via Core-Stability | UIUC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lKULHf7oFDo)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=lKULHf7oFDo&name=SUPP_material)] |\n| SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning | Sorbonne Université | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=25XIE30VHZE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01639)] |\n| FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction | MSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OtxyysUdBE)] [[CODE](https:\u002F\u002Fgithub.com\u002FMSU-MLSys-Lab\u002FFedRolex)] |\n| On Sample Optimality in Personalized Collaborative and Federated Learning | INRIA | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7EP90NMAoK)] |\n| DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing | HKUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hPkGV4BPsmv)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02680)] |\n| FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | THU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5vVSA_cdRqe)] |\n| Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning | KAUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=edkno3SvKo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.04338)] |\n| VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? | WHU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vNrSXIFJ9wz)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=edkno3SvKo&name=SUPP_material)] |\n| DENSE: Data-Free One-Shot Federated Learning | ZJU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QFQoxCFYEkA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12371)] |\n| CalFAT: Calibrated Federated Adversarial Training with Label Skewness | ZJU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8N1NDRGQSQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14926)] |\n| SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning | OSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wTp4KgVIJ5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00611)] |\n| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8SilFGuXgmk)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00690)] |\n| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness | PKU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wFymjzZEEkH)] |\n| Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sj9l1JCrAk6)] |\n| BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | UC Berkeley | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jzd2bE5MxW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.06343)] |\n| Byzantine-tolerant federated Gaussian process regression for streaming data | PSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Nx4gNemvNvx)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Nx4gNemvNvx&name=SUPP_material)] |\n| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | CMU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tz1PRT6lfLe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.09888)] |\n| Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering | Yale | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=N0tKCpMhA2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14664)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaoyuzhao123\u002Fcoreset-vfl-codes)] |\n| Communication Efficient Federated Learning for Generalized Linear Bandits | University of Virginia | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xwz9B6LDM5c)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Xwz9B6LDM5c&name=SUPP_material)] |\n| Recovering Private Text in Federated Learning of Language Models | Princeton | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dqgzfhHd2-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.08514)] [[CODE](https:\u002F\u002Fgithub.com\u002FPrinceton-SysML\u002FFILM)] |\n| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach | UTS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mhQLcMjWw75)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.10083)] |\n| Global Convergence of Federated Learning for Mixed Regression | Northeastern University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DdxNka9tMRd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07279)] |\n| Resource-Adaptive Federated Learning with All-In-One Neural Composition | JHU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wfel7CjOYk)] |\n| Self-Aware Personalized Federated Learning | Amazon | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EqJ5_hZSqgy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.08069)] |\n| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Northeastern University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TATzsweWfof)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.01132)] |\n| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fJt2KFnRqZ)] |\n| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning | EPFL | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4_oCZgBIVI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08307)] |\n| Personalized Online Federated Multi-Kernel Learning | UCI | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wUctlvhsNWg)] |\n| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | Duke University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1GAjC_FauE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.01432)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=1GAjC_FauE&name=SUPP_material)] |\n| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qSs7C7c4G8D)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13648)] |\n| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qw3MZb1Juo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.03097)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qw3MZb1Juo&name=SUPP_material)] |\n| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | University of Oxford | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mrt90D00aQX)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=mrt90D00aQX&name=SUPP_material)] |\n| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching | KAIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ql75oqz1npy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00270)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Ql75oqz1npy&name=SUPP_material)] |\n| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits | UC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Fx7oXUVEPW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03106)] |\n| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Tulane University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4OHRr7gmhd4)] |\n| On Privacy and Personalization in Cross-Silo Federated Learning | CMU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Oq2bdIQQOIZ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07902)] |\n| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | NUS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fiBnhdazkyx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06312)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FFedSim)] |\n| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | Owkin | NeurIPS Datasets and Benchmarks | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GgM5DiAb6A2)] [[CODE](https:\u002F\u002Fgithub.com\u002Fowkin\u002FFLamby)] |\n| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources | University of Pittsburgh | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftan22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06261)] [[CODE](https:\u002F\u002Fgithub.com\u002Fellenxtan\u002Fifedtree)] |\n| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fbao22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.08204)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002FFederated-Sparse-Learning)] |\n| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | NYU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fbietti22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.05318)] [[CODE](https:\u002F\u002Fgithub.com\u002Falbietz\u002Fppsgd)] |\n| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning :fire: | Stanford; Google Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fchen22c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03761)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fprivate_linear_compression)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17529.pdf)] |\n| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Google Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fchen22s.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09916)] [[CODE](https:\u002F\u002Fgithub.com\u002FWeiNingChen\u002Fpbm)] |\n| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training | USTC | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fdai22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00187)] [[CODE](https:\u002F\u002Fgithub.com\u002Frong-dai\u002FDisPFL)] |\n| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning | University of Oulu | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Felgabli22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08829)] [[CODE](https:\u002F\u002Fgithub.com\u002Faelgabli\u002FFedNew)] |\n| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning | University of Cambridge | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fhonig22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.00465)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16009.pdf)] [[CODE](https:\u002F\u002Fmedia.icml.cc\u002FConferences\u002FICML2022\u002FSUPP\u002Fhonig22a-supp.zip)] |\n| Accelerated Federated Learning with Decoupled Adaptive Optimization | Auburn University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fjin22e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.07223)] |\n| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Georgia Tech | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fkhodadadian22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10185)] |\n| Multi-Level Branched Regularization for Federated Learning | Seoul National University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fkim22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.06936)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjinkyu032\u002FFedMLB)] [[PAGE](http:\u002F\u002Fcvlab.snu.ac.kr\u002Fresearch\u002FFedMLB\u002F)] |\n| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale :fire: | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flai22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.11367)] [[CODE](https:\u002F\u002Fgithub.com\u002FSymbioticLab\u002FFedScale)] |\n| Federated Learning with Positive and Unlabeled Data | XJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flin22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10904)] [[CODE](https:\u002F\u002Fgithub.com\u002Flittlesunlxy\u002Ffedpu-torch)] |\n| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fliu22k.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002FGAMF)] |\n| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flubana22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.11506)] [[CODE](https:\u002F\u002Fgithub.com\u002Fakhilmathurs\u002Forchestra)] |\n| Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring | USTC | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fluo22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.06818)] [[CODE](https:\u002F\u002Fgithub.com\u002Fluozhengquan\u002FDFL)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16881.pdf)] [[解读](https:\u002F\u002Fwww.bilibili.com\u002Fread\u002Fcv17092678)] |\n| Architecture Agnostic Federated Learning for Neural Networks | The University of Texas at Austin | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmakhija22a.html)] [[PDF](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16926.pdf)] |\n| Personalized Federated Learning through Local Memorization | Inria | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmarfoq22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09360)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fknn-per)] |\n| Proximal and Federated Random Reshuffling | KAUST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmishchenko22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.06704)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkonstmish\u002Frr_prox_fed)] |\n| Federated Learning with Partial Model Personalization | University of Washington | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fpillutla22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03809)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkrishnap25\u002FFL_partial_personalization)] |\n| Generalized Federated Learning via Sharpness Aware Minimization | University of South Florida | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fqu22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02618)] |\n| FedNL: Making Newton-Type Methods Applicable to Federated Learning | KAUST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fsafaryan22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02969)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_VYCEWT17R0&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17084.pdf)] |\n| Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | CMU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fsharma22c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.04850)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17435.pdf)] |\n| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning | Hong Kong Baptist University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftang22d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02465)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwizard1203\u002FVHL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F548508633)] |\n| FedNest: Federated Bilevel, Minimax, and Compositional Optimization | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftarzanagh22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.02215)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmc-nya\u002FFedNest)] |\n| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | VMware Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fvargaftik22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.08842)] [[CODE](https:\u002F\u002Fgithub.com\u002Famitport\u002FEDEN-Distributed-Mean-Estimation)] |\n| Communication-Efficient Adaptive Federated Learning | Pennsylvania State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22o.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.02719)] |\n| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | CISPA Helmholz Center for Information Security | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22y.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.05323)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16194_hmjFNsN.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fa514514772\u002FProgFed)] |\n| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification :fire: | University of Maryland | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwen22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00580)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| Anarchic Federated Learning | The Ohio State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyang22r.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.09875)] |\n| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyi22a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLipingYi\u002FQSFL)] |\n| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyoon22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.11453)] |\n| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyue22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03681)] [[CODE](https:\u002F\u002Fgithub.com\u002FKAI-YUE\u002Fntk-fed)] |\n| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13673)] |\n| Personalized Federated Learning via Variational Bayesian Inference | CAS | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22o.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07977)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17302.pdf)] [[UC.](https:\u002F\u002Fgithub.com\u002FAllenBeau\u002FpFedBayes)] |\n| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] |\n| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22w.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10341)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjhcknzzm\u002FFederated-Learning-Backdoor\u002F)] |\n| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | Michigan State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhu22e.html)] |\n| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST | ICLR (oral) | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LdlwbBP2mlq)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=LdlwbBP2mlq&name=SUPP_material)] |\n| Bayesian Framework for Gradient Leakage | ETH Zurich | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f2lrIbGx3x7)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.04706)] [[CODE](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Fbayes-framework-leakage)] |\n| Federated Learning from only unlabeled data with class-conditional-sharing clients | The University of Tokyo; CUHK | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WHA8009laxu)] [[CODE](https:\u002F\u002Fgithub.com\u002Flunanbit\u002FFedUL)] |\n| FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning | CMU; University of Illinois at Urbana-Champaign; University of Washington | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZaVVVlcdaN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06869.)] |\n| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | THU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=541PxiEKN3F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.02645)] [[CODE](https:\u002F\u002Fgithub.com\u002FZoesgithub\u002FFedReg)] |\n| FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning | POSTECH | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=d71n4ftoCBy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06098)] [[CODE](https:\u002F\u002Fgithub.com\u002FSouth-hw\u002FFedPara_ICLR22)] |\n| An Agnostic Approach to Federated Learning with Class Imbalance | University of Pennsylvania | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xo0lbDt975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FFederated-Learning-Pytorch)] |\n| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; The University of Texas at Austin | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_QLmakITKg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09747)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FSplitMix)] |\n| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models :fire: | University of Maryland; NYU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fwzUgo0FM9v)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.13057)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; University of Oxford | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2sDQwC_hmnM)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.02507)] |\n| Diverse Client Selection for Federated Learning via Submodular Maximization | Intel; CMU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nwKXyFvaUm)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmelodi-lab\u002Fdivfl)] |\n| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | Purdue | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B7ZbqNLDn-_)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00280)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshams-sam\u002FFedOptim)] |\n| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions :fire: | University of Maryland; Google | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E4EE_ohFGz)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002F7525c36324cb022bc05c3fce88ef01147cae9740\u002Fperiodic_distribution_shift)] |\n| Towards Model Agnostic Federated Learning Using Knowledge Distillation | EPFL | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lQI_mZjvBxj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.15210)] [[CODE](https:\u002F\u002Fgithub.com\u002FAfoninAndrei\u002FICLR2022)] |\n| Divergence-aware Federated Self-Supervised Learning | NTU; SenseTime | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oVE1z8NlNe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04385)] [[CODE](https:\u002F\u002Fgithub.com\u002FEasyFL-AI\u002FEasyFL)] |\n| What Do We Mean by Generalization in Federated Learning? :fire: | Stanford; Google | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VimqQq-i_Q)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14216)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fgeneralization)] |\n| FedBABU: Toward Enhanced Representation for Federated Image Classification | KAIST | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HuaYQfggn5u)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06042)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjhoon-oh\u002FFedBABU)] |\n| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | EPFL | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jXKKDEi5vJt)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09365)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliehe\u002Fbyzantine-robust-noniid-optimizer)] |\n| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | Aibee | ICLR Spotlight | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7l1IjZVddDW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12467)] [[PAGE](https:\u002F\u002Firvingmeng.github.io\u002Fprojects\u002Fprivacyface\u002F)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F484920301)] |\n| Hybrid Local SGD for Federated Learning with Heterogeneous Communications | University of Texas; Pennsylvania State University | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=H0oaWl6THa)] |\n| On Bridging Generic and Personalized Federated Learning for Image Classification | The Ohio State University | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I1hQbx10Kxn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.00778)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongyouc\u002FFed-RoD)] |\n| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LdlwbBP2mlq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10342)] |\n| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. |  | JMLR | 2021 | [[PUB](http:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv22\u002F19-1048.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsabersalehk\u002FMRE_C)] |\n| Constrained differentially private federated learning for low-bandwidth devices |  | UAI | 2021 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv161\u002Fkerkouche21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00342)] |\n| Federated stochastic gradient Langevin dynamics |  | UAI | 2021 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv161\u002Fmekkaoui21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11231)] |\n| Federated Learning Based on Dynamic Regularization | BU; ARM | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B7v4QMR6Z9w)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.04263)] [[CODE](https:\u002F\u002Fgithub.com\u002FAntixK\u002FFedDyn)] |\n| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | The Ohio State University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jDdzh5ul-d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.11203)] |\n| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Duke University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TNkPBBYFkXg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.01264)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdem123456789\u002FHeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)] |\n| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ogga20D2HO-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.00233)] |\n| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms :fire: | CMU; Google | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GFsU8a0sGB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.05273)] [[CODE](https:\u002F\u002Fgithub.com\u002Falshedivat\u002Ffedpa)] |\n| Adaptive Federated Optimization :fire: | Google | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LkFG3lB13U5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.00295)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Foptimization)] |\n| Personalized Federated Learning with First Order Model Optimization | Stanford; NVIDIA | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ehJqJQk9cw)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.08565)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FFedFomo)] [[UC.](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FPFL-Non-IID)] |\n| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization :fire: | Princeton | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6YEQUn0QICG)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07623)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmed-air\u002FFedBN)] |\n| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | The Ohio State University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dgtpE6gKjHn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.01974)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongyouc\u002Ffedbe)] |\n| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ce6CFXBh30h)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12097)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwyjeong\u002FFedMatch)] |\n| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Ffeng21f.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.09757)] [[CODE](https:\u002F\u002Fgithub.com\u002FFengHZ\u002FKD3A)] [[解读](https:\u002F\u002Fmp.weixin.qq.com\u002Fs\u002FgItgiZmKUxg0ltaeOVdnRw)] |\n| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Harvard University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Flam21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06089)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38958558\u002Fgradient-disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgdisag\u002Fgradient_disaggregation)] |\n| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fhuang21c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05001)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959650\u002Fflntk-a-neural-tangent-kernelbased-framework-for-federated-learning-analysis)] |\n| Personalized Federated Learning using Hypernetworks :fire: | Bar-Ilan University; NVIDIA | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fshamsian21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04628)] [[CODE](https:\u002F\u002Fgithub.com\u002FAvivSham\u002FpFedHN)] [[PAGE](https:\u002F\u002Favivsham.github.io\u002Fpfedhn\u002F)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959583\u002Fpersonalized-federated-learning-using-hypernetworks)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F431130945)] |\n| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyuan21d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.08474)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002FFCO-ICML21)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https:\u002F\u002Fhongliny.github.io\u002Ffiles\u002FFCO_ICML21\u002FFCO_ICML21_slides.pdf)] |\n| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; University of Pennsylvania | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fcollins21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07078)] [[CODE](https:\u002F\u002Fgithub.com\u002Flgcollins\u002FFedRep)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959519\u002Fexploiting-shared-representations-for-personalized-federated-learning)] |\n| Data-Free Knowledge Distillation for Heterogeneous Federated Learning :fire: | Michigan State University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fzhu21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.10056)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhuangdizhu\u002FFedGen)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959429\u002Fdatafree-knowledge-distillation-for-heterogeneous-federated-learning)] |\n| Federated Continual Learning with Weighted Inter-client Transfer | KAIST | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyoon21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03196)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwyjeong\u002FFedWeIT)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959323\u002Ffederated-continual-learning-with-weighted-interclient-transfer)] |\n| Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity | The University of Iowa | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyuan21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.04635)] [[CODE](https:\u002F\u002Flibauc.org\u002F)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959235\u002Ffederated-deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity)] |\n| Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning | The University of Tokyo | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fmurata21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03198)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959169\u002Fbiasvariance-reduced-local-sgd-for-less-heterogeneous-federated-learning)] |\n| Federated Learning of User Verification Models Without Sharing Embeddings | Qualcomm | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fhosseini21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.08776)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38958858\u002Ffederated-learning-of-user-verification-models-without-sharing-embeddings)] |\n| Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | Accenture | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Ffraboni21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05883)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002F\u002FLabs-Federated-Learning\u002Ftree\u002Fclustered_sampling)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959618\u002Fclustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning)] |\n| Ditto: Fair and Robust Federated Learning Through Personalization | CMU; Facebook AI | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fli21h.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.04221)] [[CODE](https:\u002F\u002Fgithub.com\u002Flitian96\u002Fditto)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38955195\u002Fditto-fair-and-robust-federated-learning-through-personalization)] |\n| Heterogeneity for the Win: One-Shot Federated Clustering | CMU | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fdennis21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00697)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959380\u002Fheterogeneity-for-the-win-oneshot-federated-clustering)] |\n| The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation :fire: | Google | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fkairouz21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.06387)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fdistributed_dp)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959306\u002Fthe-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation)] |\n| Debiasing Model Updates for Improving Personalized Federated Training | BU; Arm | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Facar21a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvenkatesh-saligrama\u002FPersonalized-Federated-Learning)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959212\u002Fdebiasing-model-updates-for-improving-personalized-federated-training)] |\n| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Cornell University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fblum21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03228)] [[CODE](https:\u002F\u002Fgithub.com\u002Frlphilli\u002FCollaborative-Incentives)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959135\u002Fone-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] |\n| CRFL: Certifiably Robust Federated Learning against Backdoor Attacks | UIUC; IBM | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fxie21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08283)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FCRFL)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959047\u002Fcrfl-certifiably-robust-federated-learning-against-backdoor-attacks)] |\n| Federated Learning under Arbitrary Communication Patterns | Indiana University; Amazon | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Favdiukhin21a.html)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959048\u002Ffederated-learning-under-arbitrary-communication-patterns)] |\n| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eNB4WXnNczJ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.09461)] |\n| Boosting with Multiple Sources | Google | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1oP1duoZxx)] |\n| DRIVE: One-bit Distributed Mean Estimation | VMware | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KXRTmcv3dQ8)] [[CODE](https:\u002F\u002Fgithub.com\u002Famitport\u002FDRIVE-One-bit-Distributed-Mean-Estimation)] |\n| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yRfsADObu18)] [[CODE](https:\u002F\u002Fgithub.com\u002FXinyiYS\u002FGradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning)] |\n| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffa84632d742f2729dc32ce8cb5d49733-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14962)] [[CODE](https:\u002F\u002Fgithub.com\u002Fml-postech\u002Fgradient-inversion-generative-image-prior)] |\n| Distributed Machine Learning with Sparse Heterogeneous Data | University of Oxford | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=F9HNBbytcqT)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.01417)] |\n| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | UCLA | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SPrVNsXnGd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.08763)] |\n| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F076a8133735eb5d7552dc195b125a454-Abstract.html)] |\n| CAFE: Catastrophic Data Leakage in Vertical Federated Learning | Rensselaer Polytechnic Institute; IBM Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F08040837089cdf46631a10aca5258e16-Abstract.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FDeRafael\u002FCAFE)] |\n| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee | NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F080acdcce72c06873a773c4311c2e464-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14074)] [[CODE](https:\u002F\u002Fgithub.com\u002Fflint-xf-fan\u002FByzantine-Federeated-RL)] |\n| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Cornell University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F09a5e2a11bea20817477e0b1dfe2cc21-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09580)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkpdonahue\u002Fmodel_sharing_games)] |\n| QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | UCLA | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F1dba3025b159cd9354da65e2d0436a31-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.13892)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzkhku\u002Ffedsage)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F430789355)] |\n| The Skellam Mechanism for Differentially Private Federated Learning :fire: | Google Research; CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F285baacbdf8fda1de94b19282acd23e2-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04995)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fdistributed_dp)] |\n| No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | NUS; Huawei | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F2f2b265625d76a6704b08093c652fd79-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05001)] |\n| STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | UMN | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F3016a447172f3045b65f5fc83e04b554-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10435)] |\n| Subgraph Federated Learning with Missing Neighbor Generation | Emory;  UBC; Lehigh University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F34adeb8e3242824038aa65460a47c29e-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13430)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzkhku\u002Ffedsage)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F423555171)] |\n| Evaluating Gradient Inversion Attacks and Defenses in Federated Learning :fire: | Princeton | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F3b3fff6463464959dcd1b68d0320f781-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.00059)] [[CODE](https:\u002F\u002Fgithub.com\u002FPrinceton-SysML\u002FGradAttack)] |\n| Personalized Federated Learning With Gaussian Processes | Bar-Ilan University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F46d0671dd4117ea366031f87f3aa0093-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.15482)] [[CODE](https:\u002F\u002Fgithub.com\u002FIdanAchituve\u002FpFedGP)] |\n| Differentially Private Federated Bayesian Optimization with Distributed Exploration | MIT; NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F4c27cea8526af8cfee3be5e183ac9605-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14153)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaizhongxiang\u002FDifferentially-Private-Federated-Bayesian-Optimization)] |\n| Parameterized Knowledge Transfer for Personalized Federated Learning | PolyU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F5383c7318a3158b9bc261d0b6996f7c2-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.02862)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcugzj\u002FKT-pFL)] |\n| Federated Reconstruction: Partially Local Federated Learning :fire: | Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03448)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Freconstruction)] [[UC.](https:\u002F\u002Fgithub.com\u002FKarhouTam\u002FFedRecon)] |\n| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | THU; Princeton; MIT | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F64be20f6dd1dd46adf110cf871e3ed35-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04159)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhmgxr128\u002FMIFA_code\u002F)] |\n| FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective | Duke University; Accenture Labs | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F692baebec3bb4b53d7ebc3b9fabac31b-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.13864)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjeremy313\u002FFL-WBC)] |\n| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout | KAUST; Samsung AI Center | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F6aed000af86a084f9cb0264161e29dd3-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.13451)] |\n| Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients | University of Pennsylvania | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F7a6bda9ad6ffdac035c752743b7e9d0e-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07053)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35898)] |\n| Federated Multi-Task Learning under a Mixture of Distributions | INRIA; Accenture Labs | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F82599a4ec94aca066873c99b4c741ed8-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.10252)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002FFedEM)] |\n| Federated Graph Classification over Non-IID Graphs | Emory | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13423)] [[CODE](https:\u002F\u002Fgithub.com\u002FOxfordblue7\u002FGCFL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F430718887)] |\n| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing | CMU; Hewlett Packard Enterprise | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fa0205b87490c847182672e8d371e9948-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04502)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmkhodak\u002Ffedex)] |\n| On Large-Cohort Training for Federated Learning :fire: | Google; CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fab9ebd57177b5106ad7879f0896685d4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07820)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Ff4e26c1b9b47ac320e520a8b9943ea2c5324b8c2\u002Flarge_cohort)] |\n| DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | KAUST; Columbia University; University of Central Florida | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fb0ab42fcb7133122b38521d13da7120b-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03112)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhangxu0304\u002FDeepReduce)] |\n| PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization | Huawei | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fc429429bf1f2af051f2021dc92a8ebea-Abstract.html)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F37327)] |\n| Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fceb0595112db2513b9325a85761b7310-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.01338)] |\n| Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning | THU; Alibaba; Weill Cornell Medicine | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fdb8e1af0cb3aca1ae2d0018624204529-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.08435)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuis15\u002FFCFL)] |\n| Federated Linear Contextual Bandits | The Pennsylvania State University; Facebook; University of Virginia | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fe347c51419ffb23ca3fd5050202f9c3d-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14177)] [[CODE](https:\u002F\u002Fgithub.com\u002FRuiquan5514\u002FFederated-Linear-Contextual-Bandits)] |\n| Few-Round Learning for Federated Learning | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff065d878ccfb4cc4f4265a4ff8bafa9a-Abstract.html)] |\n| Breaking the centralized barrier for cross-device federated learning | EPFL; Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff0e6be4ce76ccfa73c5a540d992d0756-Abstract.html)] [[CODE](https:\u002F\u002Ffedjax.readthedocs.io\u002Fen\u002Flatest\u002Ffedjax.algorithms.html#module-fedjax.algorithms.mime)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F37564)] |\n| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff740c8d9c193f16d8a07d3a8a751d13f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.02083)] |\n| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazon; Google | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffc03d48253286a798f5116ec00e99b2b-Abstract.html)] [[PAGE](https:\u002F\u002Fdga.hanlab.ai\u002F)] [[SLIDE](https:\u002F\u002Fdga.hanlab.ai\u002Fassets\u002Fdga_slides.pdf)] |\n| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization | University of North Carolina at Chapel Hill; IBM Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffe7ee8fc1959cc7214fa21c4840dff0a-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03452)] [[CODE](https:\u002F\u002Fgithub.com\u002Func-optimization\u002FFedDR)] |\n| Federated Adversarial Domain Adaptation | BU; Columbia University; Rutgers University | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJezF3VYPB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02054)] [[CODE](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1OekTpqB6qLfjlE2XUjQPm3F110KDMFc0\u002Fview?usp=sharing)] |\n| DBA: Distributed Backdoor Attacks against Federated Learning | ZJU; IBM Research | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rkgyS0VFvr)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FDBA)] |\n| Fair Resource Allocation in Federated Learning :fire: | CMU; Facebook AI | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ByexElSYDr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10497)] [[CODE](https:\u002F\u002Fgithub.com\u002Flitian96\u002Ffair_flearn)] |\n| Federated Learning with Matched Averaging :fire: | University of Wisconsin-Madison; IBM Research | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BkluqlSFDS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06440)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002FFedMA)] |\n| Differentially Private Meta-Learning | CMU | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rJgqMRVYvr)] [[PDF](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] |\n| Generative Models for Effective ML on Private, Decentralized Datasets :fire: | Google | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SJgaRA4FPH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.06679)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fgans)] |\n| On the Convergence of FedAvg on Non-IID Data :fire: | PKU | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJxNAnVtDS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.02189)] [[CODE](https:\u002F\u002Fgithub.com\u002Flx10077\u002Ffedavgpy)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F500005337)] |\n| FedBoost: A Communication-Efficient Algorithm for Federated Learning | Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fhamer20a.html)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928463\u002Ffedboost-a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest)] |\n| FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; Amazon | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Frothchild20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07682)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928454\u002Ffetchsgd-communicationefficient-federated-learning-with-sketching)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkiddyboots216\u002FCommEfficient)] |\n| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fkarimireddy20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.06378)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38927610\u002Fscaffold-stochastic-controlled-averaging-for-federated-learning)] [[UC.](https:\u002F\u002Fgithub.com\u002Framshi236\u002FAccelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F538941775)] |\n| Federated Learning with Only Positive Labels | Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fyu20f.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10342)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928322\u002Ffederated-learning-with-only-positive-labels)] |\n| From Local SGD to Local Fixed-Point Methods for Federated Learning | Moscow Institute of Physics and Technology; KAUST | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fmalinovskiy20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.01442)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002FSlides\u002Ficml\u002F2020\u002Fvirtual)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928320\u002Ffrom-local-sgd-to-local-fixed-point-methods-for-federated-learning)] |\n| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization | KAUST | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fli20g.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11364)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002FSlides\u002Ficml\u002F2020\u002Fvirtual)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38927921\u002Facceleration-for-compressed-gradient-descent-in-distributed-optimization)] |\n| Differentially-Private Federated Linear Bandits | MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020\u002Fhash\u002F4311359ed4969e8401880e3c1836fbe1-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11425)] [[CODE](https:\u002F\u002Fgithub.com\u002Fabhimanyudubey\u002Fprivate_federated_linear_bandits)] |\n| Federated Principal Component Analysis | University of Cambridge; Quine Technologies | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F47a658229eb2368a99f1d032c8848542-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.08059)] [[CODE](https:\u002F\u002Fgithub.com\u002Fandylamp\u002Ffederated_pca)] |\n| FedSplit: an algorithmic framework for fast federated optimization | UC Berkeley | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F4ebd440d99504722d80de606ea8507da-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.05238)] |\n| Federated Bayesian Optimization via Thompson Sampling | NUS; MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F6dfe08eda761bd321f8a9b239f6f4ec3-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.10154)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaizhongxiang\u002FFederated_Bayesian_Optimization)] |\n| Lower Bounds and Optimal Algorithms for Personalized Federated Learning | KAUST | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F187acf7982f3c169b3075132380986e4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.02372)] |\n| Robust Federated Learning: The Case of Affine Distribution Shifts | UC Santa Barbara; MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Ff5e536083a438cec5b64a4954abc17f1-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08907)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffarzanfarnia\u002FRobustFL)] |\n| An Efficient Framework for Clustered Federated Learning | UC Berkeley; DeepMind | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe32cc80bf07915058ce90722ee17bb71-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.04088)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjichan3751\u002Fifca)] |\n| Distributionally Robust Federated Averaging :fire: | Pennsylvania State University | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12660)] [[CODE](https:\u002F\u002Fgithub.com\u002FMLOPTPSU\u002FFedTorch)] |\n| Personalized Federated Learning with Moreau Envelopes :fire: | The University of Sydney | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Ff4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08848)] [[CODE](https:\u002F\u002Fgithub.com\u002FCharlieDinh\u002FpFedMe)] |\n| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | MIT; UT Austin | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F24389bfe4fe2eba8bf9aa9203a44cdad-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.07948)] [[UC.](https:\u002F\u002Fgithub.com\u002FKarhouTam\u002FPer-FedAvg)] |\n| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge | USC | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fa1d4c20b182ad7137ab3606f0e3fc8a4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14513)] [[CODE](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FFedML\u002Ftree\u002Fmaster\u002Ffedml_experiments\u002Fdistributed\u002Ffedgkt)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F536901871)] |\n| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization :fire: | CMU; Princeton | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F564127c03caab942e503ee6f810f54fd-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07481)] [[CODE](https:\u002F\u002Fgithub.com\u002FJYWa\u002FFedNova)] [[UC.](https:\u002F\u002Fgithub.com\u002Fcarbonati\u002Ffl-zoo)] |\n| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | University of Wisconsin-Madison | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fb8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.05084)] |\n| Federated Accelerated Stochastic Gradient Descent | Stanford | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F39d0a8908fbe6c18039ea8227f827023-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08950)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002FFedAc-NeurIPS20)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FK28zpAgg3HM)] |\n| Inverting Gradients - How easy is it to break privacy in federated learning? :fire: | University of Siegen | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fc4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.14053)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Finvertinggradients)] |\n| Ensemble Distillation for Robust Model Fusion in Federated Learning | EPFL | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F18df51b97ccd68128e994804f3eccc87-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07242)] [[CODE](https:\u002F\u002Fgithub.com\u002Fepfml\u002Ffederated-learning-public-code\u002Ftree\u002Fmaster\u002Fcodes\u002FFedDF-code)] |\n| Throughput-Optimal Topology Design for Cross-Silo Federated Learning | INRIA | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe29b722e35040b88678e25a1ec032a21-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.12229)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fcommunication-in-cross-silo-fl)] |\n| Bayesian Nonparametric Federated Learning of Neural Networks :fire: | IBM | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fyurochkin19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12022)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002Fprobabilistic-federated-neural-matching)] |\n| Analyzing Federated Learning through an Adversarial Lens :fire: | Princeton; IBM | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fbhagoji19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.12470)] [[CODE](https:\u002F\u002Fgithub.com\u002Finspire-group\u002FModelPoisoning)] |\n| Agnostic Federated Learning | Google | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fmohri19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00146)] |\n| cpSGD: Communication-efficient and differentially-private distributed SGD | Princeton; Google | NeurIPS | 2018 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018\u002Fhash\u002F21ce689121e39821d07d04faab328370-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10559)] |\n| Federated Multi-Task Learning :fire: | Stanford; USC; CMU | NeurIPS | 2017 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017\u002Fhash\u002F6211080fa89981f66b1a0c9d55c61d0f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10467)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgingsmith\u002Ffmtl)] |\n\n\u003C!-- END:fl-in-top-ml-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n\n## fl in top dm conference and journal\n\nFederated Learning papers accepted by top DM(Data Mining) conference and journal, Including [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fkdd\u002Findex.html)(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fwsdm\u002Findex.html)(Web Search and Data Mining).\n\n- [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AKDD%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3690624), [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3637528), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3580305)([Research Track](https:\u002F\u002Fkdd.org\u002Fkdd2023\u002Fresearch-track-papers\u002F), [Applied Data Science track](https:\u002F\u002Fkdd.org\u002Fkdd2023\u002Fads-track-papers\u002F), [Workshop](https:\u002F\u002Ffl4data-mining.github.io\u002Fpapers\u002F)), 2022([Research Track](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperRT.html), [Applied Data Science track](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperADS.html)), [2021](https:\u002F\u002Fkdd.org\u002Fkdd2021\u002Faccepted-papers\u002Findex), [2020](https:\u002F\u002Fwww.kdd.org\u002Fkdd2020\u002Faccepted-papers)\n- [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWSDM%3A) [2025](https:\u002F\u002Fwww.wsdm-conference.org\u002F2025\u002Faccepted-papers\u002F), [2024](https:\u002F\u002Fwww.wsdm-conference.org\u002F2024\u002Faccepted-papers\u002F), [2023](https:\u002F\u002Fwww.wsdm-conference.org\u002F2023\u002Fprogram\u002Faccepted-papers), [2022](https:\u002F\u002Fwww.wsdm-conference.org\u002F2022\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww.wsdm-conference.org\u002F2021\u002Faccepted-papers.php), [2019](https:\u002F\u002Fwww.wsdm-conference.org\u002F2019\u002Faccepted-papers.php)\n\n\u003Cdetails open>\n\u003Csummary>fl in top dm conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-dm-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                   |    Venue                     |    Year    |    Materials|\n| ------------------------------------------------------------ | ---------------------------------------------------------- | ---------------------- | ---- | ------------------------------------------------------------ |\n| Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709235)] |\n| Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709341)] |\n| Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709243)] |\n| FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709346)] |\n| Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709284)] |\n| PraFFL: A Preference-Aware Scheme in Fair Federated Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709217)] |\n| Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709311)] |\n| BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709309)] |\n| Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703513)] |\n| FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703493)] |\n| Towards Personalized Federated Multi-Scenario Multi-Task Recommendation |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703523)] |\n| Density-aware and Cluster-based Federated Anomaly Detection on Data Streams |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703548)] |\n| Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3704125)] |\n| FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics |  | KDD Workshop | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671490)] |\n| Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671722)] |\n| *BadSampler:* Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671879)] |\n| Federated Graph Learning with Structure Proxy Alignment |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671717)] |\n| HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671660)] |\n| FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671545)] |\n| Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671590)] |\n| FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671573)] |\n| On the Convergence of Zeroth-Order Federated Tuning for Large Language Models |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671865)] |\n| CASA: Clustered Federated Learning with Asynchronous Clients |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671979)] |\n| FLAIM: AIM-based Synthetic Data Generation in the Federated Setting |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671990)] |\n| Privacy-Preserving Federated Learning using Flower Framework |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671447)] |\n| FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671748)] |\n| FedNLR: Federated Learning with Neuron-wise Learning Rates |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3672042)] |\n| FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671897)] |\n| FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671899)] |\n| Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671663)] |\n| PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671753)] |\n| FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671906)] |\n| FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671613)] |\n| OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671582)] |\n| Personalized Federated Continual Learning via Multi-Granularity Prompt |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671948)] |\n| Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671908)] |\n| GPFedRec: Graph-Guided Personalization for Federated Recommendation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671702)] |\n| Asynchronous Vertical Federated Learning for Kernelized AUC Maximization |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671930)] |\n| VertiMRF: Differentially Private Vertical Federated Data Synthesis |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671771)] |\n| User Consented Federated Recommender System Against Personalized Attribute Inference Attack | HKUST | WSDM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3616855.3635830)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.16203)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhkust-knowcomp\u002Fuc-fedrec)] |\n| Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning | ECNU | WSDM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3616855.3635758)] |\n| Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation | University of Cambridge | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599475)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.11050)] |\n| FedDefender: Client-Side Attack-Tolerant Federated Learning | KAIST | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599346)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09048)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdeu30303\u002Ffeddefender)] |\n| FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity | ZJU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599344)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenqincn\u002FFedAPEN)] |\n| FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis | UMBC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599348)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.05247)] |\n| ShapleyFL: Robust Federated Learning Based on Shapley Value | ZJU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599500)] [[CODE](https:\u002F\u002Fgithub.com\u002FZJU-DIVER\u002FShapleyFL-Robust-Federated-Learning-Based-on-Shapley-Value)] |\n| Federated Few-shot Learning | University of Virginia | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599347)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.10234)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsongw-sw\u002Ff2l)] |\n| Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity | SDU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599521)] |\n| Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599464)] |\n| Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining | University of Pittsburgh | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599499)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03035)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FD-AUPRC)] |\n| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599293)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05613)] |\n| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599354)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03834)] |\n| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599345)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.01217)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedcp)] |\n| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599443)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.00489)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjiayunz\u002Ffedalign)] |\n| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599311)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryzhang99\u002FDM-PFL)] |\n| FS-REAL: Towards Real-World Cross-Device Federated Learning | Alibaba Group | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599829)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13363)] |\n| FedMultimodal: A Benchmark for Multimodal Federated Learning | USC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599825)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09486)] [[CODE](https:\u002F\u002Fgithub.com\u002Fusc-sail\u002Ffed-multimodal)] |\n| PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation | RUC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599889)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.08146)] [[NEWS](http:\u002F\u002Finfo.ruc.edu.cn\u002Fxwgg\u002Fxyxw\u002Fe4c838332c3a46cd8b959be49c021bb1.htm)] |\n| Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks | HKUST; Alibaba Group | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599898)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.01677)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Fbackdoor-bench)] |\n| UA-FedRec: Untargeted Attack on Federated News Recommendation | USTC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599923)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.06701)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyjw1029\u002Fua-fedrec)] |\n| International Workshop on Federated Learning for Distributed Data Mining | MSU | KDD Workshop Summaries | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599198)] [[PAGE](https:\u002F\u002Ffl4data-mining.github.io\u002F)] |\n| Is Normalization Indispensable for Multi-domain Federated Learning? |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZiaOEg8XiGN)] |\n| Distributed Personalized Empirical Risk Minimization. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k2eYX1p-Yb)] |\n| Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aJhe-VC0Ue)] |\n| SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BVH3-XCRoN3)] |\n| Optimization of User Resources in Federated Learning for Urban Sensing Applications |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D6ZQJ-szypI)] |\n| FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nXjyCmLOYj)] |\n| Federated Graph Analytics with Differential Privacy. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yBMbtNM3GR4)] |\n| Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rAHB4qkWYz)] |\n| Uncertainty Quantification in Federated Learning for Heterogeneous Health Data |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QSQOTUVQR46)] |\n| A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pLEQFXACNA)] |\n| Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_Nsxwk3WWew)] |\n| Federated Blood Supply Chain Demand Forecasting: A Case Study. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2c0hdQDvf5g)] |\n| Stochastic Clustered Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pFvTwedsUh)] |\n| A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jg3XzuNbS-0)] |\n| Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W7LqmnU4TYZ)] |\n| FedNoisy: A Federated Noisy Label Learning Benchmark |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cXMenagKy-7)] |\n| Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DZvNrRNas6z)] |\n| Federated learning for competing risk analysis in healthcare. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=-HYSYe7uXRT)] |\n| Federated Threat Detection for Smart Home IoT rules. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SK_KfAh8MtF)] |\n| Federated Unlearning for On-Device Recommendation | UQ | WSDM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539597.3570463)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10958)] |\n| Collaboration Equilibrium in Federated Learning | THU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539237)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.07926)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuis15\u002Flearning-to-collaborate)] |\n| Connected Low-Loss Subspace Learning for a Personalization in Federated Learning | Ulsan National Institute of Science and Technology | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539254)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.07628)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvaseline555\u002Fsuperfed)] |\n| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks | University of Virginia | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539384)] |\n| Communication-Efficient Robust Federated Learning with Noisy Labels | University of Pittsburgh | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539328)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05558)] |\n| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539231)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09209)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzaixizhang\u002FFLDetector)] |\n| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539402)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.08925)] [[CODE](https:\u002F\u002Fgithub.com\u002FDi-Chai\u002FFedEval)] |\n| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539308)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15896)] |\n| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning :fire: | Alibaba | KDD (Best Paper Award) | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539112)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.05562)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope)] |\n| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539047)] [[PDF](https:\u002F\u002Fhufudb.com\u002Fstatic\u002Fpaper\u002F2022\u002FSIGKDD2022_Fed-LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F544183874)] |\n| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539039)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08036)] |\n| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539086)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08036)] |\n| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | THU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539119)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.04975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwuch15\u002FFedAttack)] |\n| PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion | The University of Queensland | WSDM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3488560.3498386)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10926)] |\n| Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467309)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14248)] |\n| FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Nanjing University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467254)] [[CODE](https:\u002F\u002Fgithub.com\u002Flxcnju\u002FFedRepo)] |\n| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467281)] [[PAGE](https:\u002F\u002Fjyhong.gitlab.io\u002Fpublication\u002Ffade2021kdd\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FFADE)] [[SLIDE](https:\u002F\u002Fjyhong.gitlab.io\u002Fpublication\u002Ffade2021kdd\u002Fslides.pdf)] |\n| Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | USC | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3447548.3467371)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmengcz13\u002FKDD2021_CNFGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F434839878)] |\n| AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | Xidian University;JD Tech | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467169)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.12519)] |\n| FLOP: Federated Learning on Medical Datasets using Partial Networks | Duke University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467185)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05218.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjianyizhang123\u002FFLOP)] |\n| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3437963.3441702)] [[CODE](https:\u002F\u002Fgithub.com\u002FMTC-ETH\u002FFederated-Learning-source)] |\n| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3437963.3441747)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhxwujinze\u002Ffederated-deep-knowledge-tracing)] |\n| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | University College Dublin | KDD | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394486.3403176)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F23422)] |\n| Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | KDD | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394486.3403298)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06197)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F23301)] |\n| Federated Online Learning to Rank with Evolution Strategies | Facebook AI Research | WSDM | 2019 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3289600.3290968)] [[CODE](http:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffoltr-es)] |\n\n\u003C!-- END:fl-in-top-dm-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n## fl in top secure conference and journal\n\nFederated Learning papers accepted by top Secure conference and journal, Including [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fsp\u002Findex.html)(IEEE Symposium on Security and Privacy), [CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fccs\u002Findex.html)(Conference on Computer and Communications Security), [USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fuss\u002Findex.html)(Usenix Security Symposium) and [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fndss\u002Findex.html)(Network and Distributed System Security Symposium).\n\n- [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsp%3A) [2025](https:\u002F\u002Fsp2025.ieee-security.org\u002Fprogram-papers.html), [2024](https:\u002F\u002Fsp2024.ieee-security.org\u002Fprogram-papers.html), [2023](https:\u002F\u002Fsp2023.ieee-security.org\u002Fprogram-papers.html), [2022](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2022\u002Fprogram-papers.html), [2019](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002Fprogram-papers.html)\n- [CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACCS%3A) [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3658644), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3576915), [2022](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2022\u002Fprogram\u002Faccepted-papers.html), [2021](https:\u002F\u002Fsigsac.org\u002Fccs\u002FCCS2021\u002Faccepted-papers.html), [2019](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2019\u002Findex.php\u002Fprogram\u002Faccepted-papers\u002F), [2017](https:\u002F\u002Facmccs.github.io\u002Fpapers\u002F)\n- [USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fuss%3A) [2023](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Ftechnical-sessions), [2022](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Ftechnical-sessions), [2020](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Ftechnical-sessions)\n- [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANDSS%3A) [2025](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2025\u002Faccepted-papers\u002F), [2024](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2024\u002Faccepted-papers\u002F), [2023](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2023\u002Faccepted-papers\u002F), [2022](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2022\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2021\u002Faccepted-papers\u002F)\n\n\u003Cdetails open>\n\u003Csummary>fl in top secure conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-secure-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                     |    Venue    |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ |\n| Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning |  | S&P | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11023255)] |\n| Practical Poisoning Attacks with Limited Byzantine Clients in Clustered Federated Learning |  | S&P | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11023464)] |\n| An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050826)] |\n| Privacy-Preserving Mutual Authentication Protocol for Federated Learning in Intelligent Transportation Systems |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050805)] |\n| FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050846)] |\n| Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fprivacy-preserving-data-deduplication-for-enhancing-federated-learning-of-language-models\u002F)] |\n| Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fscale-mia-a-scalable-model-inversion-attack-against-secure-federated-learning-via-latent-space-reconstruction\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Funknown123489\u002FScale-MIA)] |\n| URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Furvfl-undetectable-data-reconstruction-attack-on-vertical-federated-learning\u002F)] |\n| RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fraifle-reconstruction-attacks-on-interaction-based-federated-learning-with-adversarial-data-manipulation\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdzungvpham\u002Fraifle)] |\n| Byzantine-Robust Decentralized Federated Learning |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670307)] |\n| Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670365)] |\n| Cross-silo Federated Learning with Record-level Personalized Differential Privacy. |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670351)] |\n| Samplable Anonymous Aggregation for Private Federated Data Analysis |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690224)] |\n| Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690200)] |\n| Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690187)] |\n| Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690191)] |\n| Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3691411)] |\n| Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3691383)] |\n| FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffp-fed-privacy-preserving-federated-detection-of-browser-fingerprinting\u002F)] |\n| FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffreqfed-a-frequency-analysis-based-approach-for-mitigating-poisoning-attacks-in-federated-learning\u002F)] |\n| Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fautomatic-adversarial-adaption-for-stealthy-poisoning-attacks-in-federated-learning\u002F)] |\n| CrowdGuard: Federated Backdoor Detection in Federated Learning |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fcrowdguard-federated-backdoor-detection-in-federated-learning\u002F)] |\n| Protecting Label Distribution in Cross-Silo Federated Learning |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646748)] |\n| FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646613)] |\n| BadVFL: Backdoor Attacks in Vertical Federated Learning |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646664)] |\n| SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning  in Future Networks to Defend Against Data Poisoning Attacks |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646830)] |\n| Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646724)] |\n| LayerDBA: Circumventing Similarity-Based Defenses in Federated Learning |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10795458\u002F)] |\n| Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10579513\u002F)] |\n| A Performance Analysis for Confidential Federated Learning. |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10579526)] |\n| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623114)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05355)] |\n| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623212)] |\n| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | THU | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623134)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.01098)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliqi16\u002Fmartfl)] |\n| Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623193)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04030)] |\n| Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3624371)] |\n| Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3624371)] |\n| Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks | University of Massachusetts Amherst | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fmozaffari)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04350)] |\n| PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation | JHU | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fyang-yuchen)] [[CODE](https:\u002F\u002Fgithub.com\u002FBHui97\u002FPrivateFL)] |\n| Gradient Obfuscation Gives a False Sense of Security in Federated Learning | NCSU | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fyue)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.04055)] [[CODE](https:\u002F\u002Fgithub.com\u002FKAI-YUE\u002Frog)] |\n| FedVal: Different good or different bad in federated learning | AI Sweden | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fvaladi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04040)] [[CODE](https:\u002F\u002Fgithub.com\u002Fviktorvaladi\u002Ffedval)] |\n| Securing Federated Sensitive Topic Classification against Poisoning Attacks | IMDEA Networks Institute | NDSS | 2023 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fsecuring-federated-sensitive-topic-classification-against-poisoning-attacks\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.13086)] [[CODE](https:\u002F\u002Fgithub.com\u002FFRM-Sec\u002FFRM)] |\n| PPA: Preference Profiling Attack Against Federated Learning | NJUST | NDSS | 2023 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fppa-preference-profiling-attack-against-federated-learning\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.04856)] |\n| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia; TU Delft; University of Padua; Radboud University | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623114)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05355)] [[CODE](https:\u002F\u002Fgithub.com\u002FDCALab-UNIPV\u002FTurning-Privacy-preserving-Mechanisms-against-Federated-Learning)] |\n| CERBERUS: Exploring Federated Prediction of Security Events | UCL London | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560580)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.03050)] |\n| EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560611)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12727)] |\n| Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560557)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.07380)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpasquini-dario\u002Feludingsecureaggregation)] |\n| Federated Boosted Decision Trees with Differential Privacy | University of Warwick | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560687)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02910)] [[CODE](https:\u002F\u002Fgithub.com\u002FSamuel-Maddock\u002Ffederated-boosted-dp-trees)] |\n| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Duke University | S&P | 2023 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2023\u002F933600a326\u002F1He7Y3q8FMY)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10936)] |\n| Scalable and Privacy-Preserving Federated Principal Component Analysis | EPFL; Tune Insight SA | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179350)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.00129)] |\n| SafeFL: MPC-friendly Framework for Private and Robust Federated Learning | TU Darmstadt | S&P Workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10188630)] |\n| On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10188636)] |\n| BayBFed: Bayesian Backdoor Defense for Federated Learning | TU Darmstadt; UTSA | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179362)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09508)] |\n| 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning | PolyU | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179401)] [[CODE](https:\u002F\u002Fgithub.com\u002FhaoyangliASTAPLE\u002F3DFed)] |\n| RoFL: Robustness of Secure Federated Learning. | ETH Zurich | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179400)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03311)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpps-lab\u002Frofl-project-code)] |\n| Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. | upenn | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179434)] [[CODE](https:\u002F\u002Fgithub.com\u002Feniac\u002Fflamingo)] |\n| ELSA: Secure Aggregation for Federated Learning with Malicious Actors. |  | S&P | 2023 |  |\n| Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy | Fudan University | S&P | 2023 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2023\u002F933600a076\u002F1He7XMLcnsc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.08662)] |\n| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | University of Massachusetts | S&P | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9833647\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tQv3CpxIyvs)] |\n| SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost | Microsoft Research | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fchandran)] [[PDF](https:\u002F\u002Feprint.iacr.org\u002F2021\u002F1538)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshahakash28\u002Fsimc)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0Oaqi0JHUac)] [[SUPP](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fusenixsecurity22-chandran.pdf)] |\n| Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors | University of Vermont | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fstevens)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-stevens.pdf)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9kYHQkr6DuE)] |\n| Label Inference Attacks Against Vertical Federated Learning | ZJU | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Ffu-chong)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-fu-chong.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FFuChong-cyber\u002Flabel-inference-attacks)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JEmRbDtosVw)] |\n| FLAME: Taming Backdoors in Federated Learning | Technical University of Darmstadt | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fnguyen)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-nguyen.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.02281)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=nMrte2S9U68)] |\n| Local and Central Differential Privacy for Robustness and Privacy in Federated Learning | University at Buffalo, SUNY | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-204\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.03561)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_aH2j5A3608&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=3)] [[UC.](https:\u002F\u002Fgithub.com\u002Fwenzhu23333\u002FDifferential-Privacy-Based-Federated-Learning)] |\n| Interpretable Federated Transformer Log Learning for Cloud Threat Forensics | University of the Incarnate Word | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-236\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3HoysA6hsC8&list=PLfUWWM-POgQsS08uHJUJI6sawDO_3sNh0&index=3)] [[UC.](https:\u002F\u002Fgithub.com\u002Fcyberthreat-datasets\u002Fctdd-2021-os-syslogs)] |\n| FedCRI: Federated Mobile Cyber-Risk Intelligence | Technical University of Darmstadt | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-229\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2zmdPqCCFxg&list=PLfUWWM-POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3)] |\n| DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection | Technical University of Darmstadt | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-205\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.00763)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MJF_7vnoGh4&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=4)] |\n| Private Hierarchical Clustering in Federated Networks | NUS | CCS | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460120.3484822)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09057)] |\n| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Duke University | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffltrust-byzantine-robust-federated-learning-via-trust-bootstrapping\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13995)] [[CODE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002Ffltrust.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zhhdPgKPCN0&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=2)] [[SLIDE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002FSecure_Federated_Learning.pdf)] |\n| POSEIDON: Privacy-Preserving Federated Neural Network Learning | EPFL | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fposeidon-privacy-preserving-federated-neural-network-learning\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kX6-PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1)] |\n| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning | University of Massachusetts Amherst | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fmanipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvrt1shjwlkr\u002FNDSS21-Model-Poisoning)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3)] |\n| SAFELearn: Secure Aggregation for private FEderated Learning | TU Darmstadt | S&P Workshop | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9474309)] |\n| Local Model Poisoning Attacks to Byzantine-Robust Federated Learning | The Ohio State University | USENIX Security | 2020 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Fpresentation\u002Ffang)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.11815)] [[CODE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002Ffltrust.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SQ12UpYrUVU&feature=emb_imp_woyt)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec20_slides_fang.pdf)] |\n| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | University of Kansas | CCS (Poster) | 2019 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3319535.3363256)] |\n| IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning | Université du Québéc á Montréal | S&P Workshop | 2019 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8844592)] |\n| Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning :fire: | University of Massachusetts Amherst | S&P | 2019 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2019\u002F666000a739\u002F1dlwhtj4r7O)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FlzJY4BjCxTc)] [[SLIDE](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002FSP19-Slides-pdfs\u002FMilad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_)] [[CODE](https:\u002F\u002Fgithub.com\u002Fprivacytrustlab\u002Fml_privacy_meter)] |\n| Practical Secure Aggregation for Privacy Preserving Machine Learning | Google | CCS | 2017 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3133956.3133982)] [[PDF](https:\u002F\u002Feprint.iacr.org\u002F2017\u002F281)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F445656765)] [[UC.](https:\u002F\u002Fgithub.com\u002FChen-Junbao\u002FSecureAggregation)] [[UC](https:\u002F\u002Fgithub.com\u002Fcorentingiraud\u002Ffederated-learning-secure-aggregation)] |\n\n\u003C!-- END:fl-in-top-secure-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n\n## fl in top cv conference and journal\n\nFederated Learning papers accepted by top CV(computer vision) conference and journal, Including [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fcvpr\u002Findex.html)(Computer Vision and Pattern Recognition), [ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficcv\u002Findex.html)(IEEE International Conference on Computer Vision), [ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Feccv\u002Findex.html)(European Conference on Computer Vision), [MM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fmm\u002Findex.html)(ACM International Conference on Multimedia), [IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fijcv\u002Findex.html)(International Journal of Computer Vision).\n\n- [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACVPR%3A) [2025](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2025?day=all), [2024](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2024?day=all), [2023](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2023?day=all), [2022](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2022), [2021](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2021?day=all)\n- [ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICCV%3A) [2023](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2023?day=all), [2021](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2021?day=all)\n- [ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AECCV%3A) [2024](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [2022](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [2020](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php)\n- [MM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fmm%3A) [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3664647), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3581783), [2022](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fmm\u002Fmm2022.html), [2021](https:\u002F\u002F2021.acmmm.org\u002Fmain-track-list), [2020](https:\u002F\u002F2020.acmmm.org\u002Fmain-track-list.html)\n- [IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fijcv%3A) 2025, 2024\n\n\u003Cdetails open>\n\u003Csummary>fl in top cv conference and journal\u003C\u002Fsummary>\n\n\u003C!-- START:fl-in-top-cv-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                     |    Venue    |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ |\n| Federated Learning with Domain Shift Eraser |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FWang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.html)] |\n| Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FCaldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpietrocagnasso\u002Ffedgloss)] |\n| FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FChen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaokunChen245\u002FFedBiP)] |\n| FedCS: Coreset Selection for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FHao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.html)] |\n| AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FHe_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html)] |\n| NoT: Federated Unlearning via Weight Negation |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FKhalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.html)] |\n| Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FKumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.html)] |\n| Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FLi_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html)] |\n| Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FLiu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FJay-Codeman\u002FSAGE)] |\n| Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FMa_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html)] |\n| HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FRaswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.html)] [[COCE](https:\u002F\u002Flalakitchen.github.io\u002FHistoFS\u002F)] |\n| F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective  Meta-Heuristics |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FSaha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.html)] [[PAGE](https:\u002F\u002Fpramitsaha.github.io\u002FFOCUS\u002F)] |\n| FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FShi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html)] |\n| FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FTan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FOakleyTan\u002FFedSPA)] |\n| Population Normalization for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FWang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.html)] |\n| Model Poisoning Attacks to Federated Learning via Multi-Round Consistency |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxyq7\u002FPoisonedFL\u002F)] |\n| dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.html)] |\n| Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FAlignIns)] |\n| A Simple Data Augmentation for Feature Distribution Skewed Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FIAMJackYan\u002FFedRDN)] |\n| Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html)] |\n| Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAVC2-UESTC\u002FFedSCE.git)] |\n| pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.html)] |\n| FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.html)] |\n| Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.html)] |\n| FedMIA: An Effective Membership Inference Attack Exploiting \"All for One\" Principle in Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiar-Mask\u002FFedMIA)] |\n| Patient-Level Anatomy Meets Scanning-Level Physics: Personalized  Federated Low-Dose CT Denoising Empowered by Large Language Model |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.html)] |\n| Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning |  | IJCV | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-024-02330-1)] |\n| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681260)] |\n| One-shot-but-not-degraded Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680715)] |\n| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681384)] |\n| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681490)] |\n| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681588)] |\n| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680867)] |\n| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680733)] |\n| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681545)] |\n| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681054)] |\n| FedSLS: Exploring Federated Aggregation in Saliency Latent Space |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681278)] |\n| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680788)] |\n| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3664647.3681094)] |\n| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data |  | MM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3664647.3681480)] |\n| Cross-Modal Meta Consensus for Heterogeneous Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681510)] |\n| Masked Random Noise for Communication-Efficient Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680608)] |\n| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681302)] |\n| Adaptive Hierarchical Aggregation for Federated Object Detection |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681158)] |\n| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681319)] |\n| Federated Fuzzy C-means with Schatten-p Norm Minimization |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681557)] |\n| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681415)] |\n| Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification |  | IJCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-024-02077-9)] |\n| SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72655-2_17)] [[CODE](https:\u002F\u002Fgithub.com\u002FKoalaYan\u002FSkyMask)] |\n| FedHide: Federated Learning by Hiding in the Neighbors |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72897-6_23)] |\n| FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73668-1_14)] |\n| FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73195-2_20)] |\n| Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73030-6_10)] |\n| Federated Learning with Local Openset Noisy Labels |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72754-2_3)] |\n| FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73010-8_22)] |\n| Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73004-7_11)] |\n| BAFFLE: A Baseline of Backpropagation-Free Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73226-3_6)] |\n| PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73650-6_9)] |\n| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72633-0_14)] |\n| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73229-4_2)] |\n| FedHARM: Harmonizing Model Architectural Diversity in Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73036-8_3)] |\n| SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72986-7_10)] |\n| Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72952-2_8)] |\n| Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73404-5_18)] |\n| Towards Multi-modal Transformers in Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72633-0_13)] |\n| Local and Global Flatness for Federated Domain Generalization |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73010-8_5)] |\n| Feature Diversification and Adaptation for Federated Domain Generalization |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73220-1_4)] |\n| PFEDEDIT: Personalized Federated Learning via Automated Model Editing |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72986-7_6)] |\n| FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | SJTU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLu_FedHCA2_Towards_Hetero-Client_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.13250)] [[CODE](https:\u002F\u002Fgithub.com\u002Finnovator-zero\u002FFedHCA2)] |\n| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | WHU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Fair_Federated_Learning_under_Domain_Skew_with_Local_Consistency_and_CVPR_2024_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.16585)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuhangchen0\u002FFedHEAL)] |\n| Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts | NWPU; HKUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Think_Twice_Before_Selection_Federated_Evidential_Active_Learning_for_Medical_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FChen_Think_Twice_Before_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2312.02567)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiayiChen815\u002FFEAL)] |\n| FedMef: Towards Memory-efficient Federated Dynamic Pruning | CUHK | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FHuang_FedMef_Towards_Memory-efficient_Federated_Dynamic_Pruning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FHuang_FedMef_Towards_Memory-efficient_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14737)] |\n| Communication-Efficient Federated Learning with Accelerated Client Gradient | SNU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FKim_Communication-Efficient_Federated_Learning_with_Accelerated_Client_Gradient_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FKim_Communication-Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2201.03172)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgeehokim\u002FFedACG)] |\n| Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space | IITH | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FKumar_Revamping_Federated_Learning_Security_from_a_Defenders_Perspective_A_Unified_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FKumar_Revamping_Federated_Learning_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FNaveenKumar-1311\u002FFCD)] |\n| Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning | TJUT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FQi_Adaptive_Hyper-graph_Aggregation_for_Modality-Agnostic_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FQi_Adaptive_Hyper-graph_Aggregation_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FMM-Fed\u002FHAMFL)] |\n| Towards Efficient Replay in Federated Incremental Learning | HUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Towards_Efficient_Replay_in_Federated_Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Towards_Efficient_Replay_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.05890)] |\n| Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices | UT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Mixed-Precision_Quantization_for_Federated_Learning_on_Resource-Constrained_Heterogeneous_Devices_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FChen_Mixed-Precision_Quantization_for_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2311.18129)] |\n| Data Valuation and Detections in Federated Learning | NUS | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Data_Valuation_and_Detections_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Data_Valuation_and_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05304)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmuz1lee\u002Fmotdata)] |\n| Decentralized Directed Collaboration for Personalized Federated Learning | NJUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLiu_Decentralized_Directed_Collaboration_for_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLiu_Decentralized_Directed_Collaboration_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.17876)] |\n| Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning | UBC | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FDeng_Unlocking_the_Potential_of_Prompt-Tuning_in_Bridging_Generalized_and_Personalized_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FDeng_Unlocking_the_Potential_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18285)] [[CODE](https:\u002F\u002Fgithub.com\u002Fubc-tea\u002FSGPT)] |\n| Global and Local Prompts Cooperation via Optimal Transport for Federated Learning | ShanghaiTech University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Global_and_Local_Prompts_Cooperation_via_Optimal_Transport_for_Federated_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Global_and_Local_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.00041)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongxialee\u002Ffedotp)] |\n| Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data | ZJU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLiao_Rethinking_the_Representation_in_Federated_Unsupervised_Learning_with_Non-IID_Data_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLiao_Rethinking_the_Representation_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.16398)] [[CODE](https:\u002F\u002Fgithub.com\u002FXeniaLLL\u002FFedU2)] |\n| Relaxed Contrastive Learning for Federated Learning | SNU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSeo_Relaxed_Contrastive_Learning_for_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSeo_Relaxed_Contrastive_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2401.04928)] [[CODE](https:\u002F\u002Fgithub.com\u002Fskynbe\u002FFedRCL)] |\n| Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning | Purdue University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhao_Leak_and_Learn_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.18144)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ovmSnjSOcks)] |\n| Traceable Federated Continual Learning | BUPT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FWang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FWang_Traceable_Federated_Continual_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FP0werWeirdo\u002FTagFCL)] |\n| Federated Online Adaptation for Deep Stereo | University of Bologna | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FPoggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FPoggi_Federated_Online_Adaptation_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.14873)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmattpoggi\u002Ffedstereo)] [[PAGE](https:\u002F\u002Ffedstereo.github.io\u002F)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FgVpWsjrUTJc)] |\n| Federated Generalized Category Discovery | UniTn | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FPu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FPu_Federated_Generalized_Category_CVPR_2024_supplemental.zip)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14107)] [[CODE](https:\u002F\u002Fgithub.com\u002FTPCD\u002FFedGCD)] |\n| Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization | ND | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLe_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLe_Efficiently_Assemble_Normalization_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.15605)] [[CODE](https:\u002F\u002Fgithub.com\u002Flhkhiem28\u002FgPerXAN?utm_source=catalyzex.com)] |\n| Text-Enhanced Data-free Approach for Federated Class-Incremental Learning | Monash University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FTran_Text-Enhanced_Data-free_Approach_for_Federated_Class-Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FTran_Text-Enhanced_Data-free_Approach_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14101)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftmtuan1307\u002Flander)] |\n| PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | UIUC; NVIDIA | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FXie_PerAda_Parameter-Efficient_Federated_Learning_Personalization_with_Generalization_Guarantees_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FXie_PerAda_Parameter-Efficient_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06637)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FPerAda)] |\n| FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning | KAIST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLee_FedSOL_Stabilized_Orthogonal_Learning_with_Proximal_Restrictions_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLee_FedSOL_Stabilized_Orthogonal_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12532)] [[CODE](https:\u002F\u002Fgithub.com\u002FLee-Gihun\u002FFedSOL)] |\n| FedUV: Uniformity and Variance for Heterogeneous Federated Learning | UC Davis | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSon_FedUV_Uniformity_and_Variance_for_Heterogeneous_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSon_FedUV_Uniformity_and_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2402.18372)] |\n| FedAS: Bridging Inconsistency in Personalized Federated Learning | WHU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FYang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxiyuanyang45\u002FFedAS)] |\n| FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Lapis Labs | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FTamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FTamirisa_FedSelect_Personalized_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.02478)] [[CODE](https:\u002F\u002Fgithub.com\u002Flapisrocks\u002Ffedselect)] |\n| Device-Wise Federated Network Pruning | PITT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FGao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FGao_Device-Wise_Federated_Network_CVPR_2024_supplemental.pdf)] |\n| Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping | HNU; PolyU; AIRS | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSun_Byzantine-robust_Decentralized_Federated_Learning_via_Dual-domain_Clustering_and_Trust_Bootstrapping_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSun_Byzantine-robust_Decentralized_Federated_CVPR_2024_supplemental.pdf)] |\n| DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning | HKUST; PolyU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FBai_DiPrompT_Disentangled_Prompt_Tuning_for_Multiple_Latent_Domain_Generalization_in_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FBai_DiPrompT_Disentangled_Prompt_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.08506)] |\n| An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning | SJTU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhang_An_Upload-Efficient_Scheme_for_Transferring_Knowledge_From_a_Server-Side_Pre-trained_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhang_An_Upload-Efficient_Scheme_CVPR_2024_supplemental.zip)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.15760)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedktl)] [[POSTER](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedKTL\u002Fblob\u002Fmain\u002FFedKTL.png)] [[SLIDES](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedKTL\u002Fblob\u002Fmain\u002FFedKTL.pdf)] |\n| An Aggregation-Free Federated Learning for Tackling Data Heterogeneity | A* STAR | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FWang_An_Aggregation-Free_Federated_Learning_for_Tackling_Data_Heterogeneity_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FWang_An_Aggregation-Free_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.18962)] |\n| FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning | BUAA; HKU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhang_FLHetBench_Benchmarking_Device_and_State_Heterogeneity_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhang_FLHetBench_Benchmarking_Device_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FCarkham\u002FFLHetBench)] [[PAGE](https:\u002F\u002Fcarkham.github.io\u002FFL_Het_Bench\u002F)] [[POSTER](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1Ln0cnptSn5EfML6ughQ7NowwjjLfMYgu\u002Fview?usp=sharing)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zDGPt3929l8)] |\n| Collaborative Visual Place Recognition through Federated Learning |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FDutto_Collaborative_Visual_Place_Recognition_through_Federated_Learning_CVPRW_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fsupplemental\u002FDutto_Collaborative_Visual_Place_CVPRW_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2404.13324)] |\n| FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FGao_FedProK_Trustworthy_Federated_Class-Incremental_Learning_via_Prototypical_Feature_Knowledge_Transfer_CVPRW_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fsupplemental\u002FGao_FedProK_Trustworthy_Federated_CVPRW_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.02685)] |\n| Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FNakka_Federated_Hyperparameter_Optimization_Through_Reward-Based_Strategies_Challenges_and_Insights_CVPRW_2024_paper.html)] |\n| On the Efficiency of Privacy Attacks in Federated Learning |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FTabassum_On_the_Efficiency_of_Privacy_Attacks_in_Federated_Learning_CVPRW_2024_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2404.09430)] |\n| FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612217)] |\n| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611958)] |\n| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612597)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.02416)] [[CODE](https:\u002F\u002Fgithub.com\u002FJ1nqianChen\u002FFedKA)] |\n| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612027)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13697)] |\n| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611953)] |\n| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3613837)] [[CODE](https:\u002F\u002Fgithub.com\u002Fexquisite1210\u002FPT-FUCH_P)] |\n| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612178)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.11646)] |\n| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611966)] |\n| Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611984)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnma3mz\u002FFedLMD)] |\n| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612481)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03457)] [[CODE](https:\u002F\u002Fgithub.com\u002Fqizhuang-qz\u002FFedCSPC)] |\n| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612474)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03331)] |\n| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611757)] |\n| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611781)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13137)] [[CODE](https:\u002F\u002Fgithub.com\u002FLipingYi\u002FFedGH)] |\n| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612134)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03432)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuxuanzhang0713\u002Ffedcsr)] |\n| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612442)] [[CODE](https:\u002F\u002Fgithub.com\u002FAffectFAL\u002FAffectFAL)] |\n| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612350)] |\n| Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHan_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09318)] [[CODE](https:\u002F\u002Fgithub.com\u002FSungwon-Han\u002FFEDCPA)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHan_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] |\n| Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; NVIDIA | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FYang_Efficient_Model_Personalization_in_Federated_Learning_via_Client-Specific_Prompt_Generation_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.15367)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FYang_Efficient_Model_Personalization_ICCV_2023_supplemental.pdf)] |\n| Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fczhang024\u002FCI-Net)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] |\n| GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning | SJTU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_GPFL_Simultaneously_Learning_Global_and_Personalized_Feature_Information_for_Personalized_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.10279)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FGPFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhang_GPFL_Simultaneously_Learning_ICCV_2023_supplemental.zip)] |\n| Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization | University of Houston | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FChen_Workie-Talkie_Accelerating_Federated_Learning_by_Overlapping_Computing_and_Communications_via_ICCV_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FChen_Workie-Talkie_Accelerating_Federated_ICCV_2023_supplemental.pdf)] |\n| PGFed: Personalize Each Client's Global Objective for Federated Learning | University of Pittsburgh | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FLuo_PGFed_Personalize_Each_Clients_Global_Objective_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01448)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljaiverson\u002Fpgfed)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FLuo_PGFed_Personalize_Each_ICCV_2023_supplemental.pdf)] |\n| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FSun_FedPerfix_Towards_Partial_Model_Personalization_of_Vision_Transformers_in_Federated_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09160)] [[CODE](https:\u002F\u002Fgithub.com\u002Fimguangyu\u002Ffedperfix)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FSun_FedPerfix_Towards_Partial_ICCV_2023_supplemental.pdf)] |\n| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FRehman_L-DAWA_Layer-wise_Divergence_Aware_Weight_Aggregation_in_Federated_Self-Supervised_Visual_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.07393)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FRehman_L-DAWA_Layer-wise_Divergence_ICCV_2023_supplemental.pdf)] |\n| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FYang_FedPD_Federated_Open_Set_Recognition_with_Parameter_Disentanglement_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FCityU-AIM-Group\u002FFedPD)] |\n| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_TARGET_Federated_Class-Continual_Learning_via_Exemplar-Free_Distillation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06937)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzj-jayzhang\u002FFederated-Class-Continual-Learning)] |\n| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FFeng_Towards_Instance-adaptive_Inference_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.06051)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchunmeifeng\u002Ffedins)] |\n| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhou_Communication-efficient_Federated_Learning_with_Single-Step_Synthetic_Features_Compressor_for_Faster_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2302.13562)] [[CODE](https:\u002F\u002Fgithub.com\u002FSoptq\u002Ficcv23-3sfc)] |\n| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FGhodsi_zPROBE_Zero_Peek_Robustness_Checks_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2206.12100)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FGhodsi_zPROBE_Zero_Peek_ICCV_2023_supplemental.pdf)] |\n| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FKim_ProtoFL_Unsupervised_Federated_Learning_via_Prototypical_Distillation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.12450)] |\n| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhuang_MAS_Towards_Resource-Efficient_Federated_Multiple-Task_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11285)] [[CODE](https:\u002F\u002Fgithub.com\u002FEasyFL-AI\u002FEasyFL\u002Ftree\u002Fmaster\u002Fapplications\u002Fmas)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhuang_MAS_Towards_Resource-Efficient_ICCV_2023_supplemental.pdf)] |\n| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FGuo_FSAR_Federated_Skeleton-based_Action_Recognition_with_Adaptive_Topology_Structure_and_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2306.11046)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FGuo_FSAR_Federated_Skeleton-based_ICCV_2023_supplemental.pdf)] |\n| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FVahidian_When_Do_Curricula_Work_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2212.12712)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FVahidian_When_Do_Curricula_ICCV_2023_supplemental.pdf)] |\n| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Duke University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FSun_Communication-Efficient_Vertical_Federated_Learning_with_Limited_Overlapping_Samples_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16270)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare\u002Ftree\u002Fmain\u002Fresearch\u002Fone-shot-vfl)] |\n| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | SCUT | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHuang_Multi-Metrics_Adaptively_Identifies_Backdoors_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06601)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsiquanhuang\u002FMulti-metrics_against_backdoors_in_FL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHuang_Multi-Metrics_Adaptively_Identifies_ICCV_2023_supplemental.pdf)] |\n| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | ZJU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FLi_No_Fear_of_Classifier_Biases_Neural_Collapse_Inspired_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10058)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzexilee\u002Ficcv-2023-fedetf)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FLi_No_Fear_of_ICCV_2023_supplemental.pdf)] |\n| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation | Ludwig Maximilian University of Munich; Siemens Technology | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FChen_FRAug_Tackling_Federated_Learning_with_Non-IID_Features_via_Representation_Augmentation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14900)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FChen_FRAug_Tackling_Federated_ICCV_2023_supplemental.pdf)] |\n| Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | BUAA | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FWu_Bold_but_Cautious_Unlocking_the_Potential_of_Personalized_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2309.11103)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkxzxvbk\u002FFling)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FWu_Bold_but_Cautious_ICCV_2023_supplemental.pdf)] |\n| Global Balanced Experts for Federated Long-Tailed Learning | CUHK-Shenzhen | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZeng_Global_Balanced_Experts_for_Federated_Long-Tailed_Learning_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FSpinozaaa\u002FFederated-Long-tailed-Learning)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZeng_Global_Balanced_Experts_ICCV_2023_supplemental.pdf)] |\n| Knowledge-Aware Federated Active Learning with Non-IID Data | The University of Sydney | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FCao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13579)] [[CODE](https:\u002F\u002Fgithub.com\u002Fycao5602\u002FKAFAL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FCao_Knowledge-Aware_Federated_Active_ICCV_2023_supplemental.pdf)] |\n| Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation | BUPT | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FWan_Enhancing_Privacy_Preservation_in_Federated_Learning_via_Learning_Rate_Perturbation_ICCV_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FWan_Enhancing_Privacy_Preservation_ICCV_2023_supplemental.pdf)] |\n| Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels | CMU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FCho_Local_or_Global_Selective_Knowledge_Assimilation_for_Federated_Learning_with_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.08809)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FCho_Local_or_Global_ICCV_2023_supplemental.pdf)] |\n| Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat | Rice University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHu_Federated_Learning_Over_Images_Vertical_Decompositions_and_Pre-Trained_Backbones_Are_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2309.03237)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhuerdong\u002FFedVert-Experiments)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHu_Federated_Learning_Over_ICCV_2023_supplemental.pdf)] |\n| Robust Heterogeneous Federated Learning under Data Corruption | WHU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FFang_Robust_Heterogeneous_Federated_Learning_under_Data_Corruption_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FFangXiuwen\u002FAugHFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FFang_Robust_Heterogeneous_Federated_ICCV_2023_supplemental.pdf)] |\n| Personalized Semantics Excitation for Federated Image Classification | Tulane University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FXia_Personalized_Semantics_Excitation_for_Federated_Image_Classification_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaifengXia\u002FPSE)] |\n| Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology | AIOZ | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FDo_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09657)] [[CODE](https:\u002F\u002Fgithub.com\u002Faioz-ai\u002FMultigraphFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FDo_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_supplemental.pdf)] |\n| Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. | Politecnico di Torino | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350693)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01366)] |\n| Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. | University of Catania | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350429)] |\n| FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350898)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchatzikon\u002FFedRCIL)] |\n| FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350371)] |\n| Rethinking Federated Learning With Domain Shift: A Prototype View | WHU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FHuang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FRethinkFL)] |\n| Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning | ECNU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fminglllli\u002FCBAFed)] |\n| DaFKD: Domain-Aware Federated Knowledge Distillation | HUST | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FWang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaozhaowang\u002FDaFKD2023)] |\n| The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Purdue University | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.14868)] |\n| FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation | ZJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FMiao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html)] |\n| On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | DTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02191)] |\n| Elastic Aggregation for Federated Optimization | Meituan | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FChen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html)] |\n| FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | UCLA | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FXiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09653)] |\n| Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | UM | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLiao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html)] |\n| ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | GaTech | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FIlhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002Fscale-fl)] |\n| Reliable and Interpretable Personalized Federated Learning | TJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FQin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html)] |\n| Federated Domain Generalization With Generalization Adjustment | SJTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedDG-GA)] |\n| Make Landscape Flatter in Differentially Private Federated Learning | THU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FShi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.11242)] [[CODE](https:\u002F\u002Fgithub.com\u002FYMJS-Irfan\u002FDP-FedSAM)] |\n| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Leuven | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12557)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjunyizhu-ai\u002Fconfidence_aware_pfl)] |\n| STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | GaTech | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FChow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.11511)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002FSTDLens)] |\n| Re-Thinking Federated Active Learning Based on Inter-Class Diversity | KAIST | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FKim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.12317)] [[CODE](https:\u002F\u002Fgithub.com\u002Fraymin0223\u002FLoGo)] |\n| Learning Federated Visual Prompt in Null Space for MRI Reconstruction | A*STAR | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FFeng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16181)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchunmeifeng\u002FFedPR)] |\n| Fair Federated Medical Image Segmentation via Client Contribution Estimation | CUHK | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FJiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16520)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare\u002Ftree\u002Fdev\u002Fresearch\u002Ffed-ce)] |\n| Federated Learning With Data-Agnostic Distribution Fusion | NJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FDuan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiruichenSpace\u002FFedFusion)] |\n| How To Prevent the Poor Performance Clients for Personalized Federated Learning? | CSU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FQu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html)] |\n| GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | ECNU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLuo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2302.14307)] [[CODE](https:\u002F\u002Fgithub.com\u002Flkyddd\u002Fgradma)] |\n| Bias-Eliminating Augmentation Learning for Debiased Federated Learning | NTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FXu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.html)] |\n| Federated Incremental Semantic Segmentation | CAS; UCAS | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FDong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04620)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiahuaDong\u002FFISS)] |\n| Asynchronous Federated Continual Learning | University of Padova | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FShenaj_Asynchronous_Federated_Continual_Learning_CVPRW_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03626)] [[SILDES](https:\u002F\u002Fgithub.com\u002FLTTM\u002FFedSpace\u002Fblob\u002Fmain\u002Fmedia\u002Fslides.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FLTTM\u002FFedSpace)] |\n| Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks | UMBC | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FOvi_Mixed_Quantization_Enabled_Federated_Learning_To_Tackle_Gradient_Inversion_Attacks_CVPRW_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FPretomRoy\u002FDefense-against-grad-inversion-attacks)] |\n| OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework | Meituan | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FChen_OpenFed_A_Comprehensive_and_Versatile_Open-Source_Federated_Learning_Framework_CVPRW_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.07852)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederalLab\u002FOpenFed)] |\n| Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | utexas | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FChen_Federated_Learning_in_Non-IID_Settings_Aided_by_Differentially_Private_Synthetic_CVPRW_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fsupplemental\u002FChen_Federated_Learning_in_CVPRW_2023_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00686)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcitychan\u002Ffederated-dpms)] |\n| TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training | USC | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FZhang_TimelyFL_Heterogeneity-Aware_Asynchronous_Federated_Learning_With_Adaptive_Partial_Training_CVPRW_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2304.06947)] |\n| Many-Task Federated Learning: A New Problem Setting and a Simple Baseline | utexas | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FCai_Many-Task_Federated_Learning_A_New_Problem_Setting_and_a_Simple_CVPRW_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FVITA-Group\u002FMaT-FL)] |\n| Confederated Learning: Going Beyond Centralization | CAS;  UCAS | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548157)] |\n| Few-Shot Model Agnostic Federated Learning | WHU | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548764)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FFSMAFL)] |\n| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548278)] |\n| FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6634_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136720069-supp.pdf)] |\n| Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F1129_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136810431-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.06338)] [[CODE](https:\u002F\u002Fgithub.com\u002Fguopengf\u002FAuto-FedRL)] |\n| Improving Generalization in Federated Learning by Seeking Flat Minima | Politecnico di Torino | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F7093_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136830636-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11834)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdebcaldarola\u002Ffedsam)] |\n| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F8092_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136830690-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13170)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvarnio\u002Ffedsim)] [[PAGE](https:\u002F\u002Ffedsim.varnio.com\u002Fen\u002Flatest\u002F)] |\n| SphereFed: Hyperspherical Federated Learning |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F2255_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136860161-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09413)] |\n| Federated Self-Supervised Learning for Video Understanding |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F7693_ECCV_2022_paper.php)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.01975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyasar-rehman\u002Ffedvssl)] |\n| FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6298_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136960673-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14936)] [[CODE](https:\u002F\u002Fgithub.com\u002Feric-ai-lab\u002FFedVLN)] |\n| Addressing Heterogeneity in Federated Learning via Distributional Transformation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6551_ECCV_2022_paper.php)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhyhmia\u002FDisTrans)] |\n| FedX: Unsupervised Federated Learning with Cross Knowledge Distillation | KAIST | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F3932_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136900682-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09158)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsungwon-han\u002Ffedx)] |\n| Personalizing Federated Medical Image Segmentation via Local Calibration | Xiamen University | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F1626_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136810449-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.04655)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjcwang123\u002Ffedlc)] |\n| ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | HIT | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FWang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html)] |\n| Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Stanford | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FQu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FQu_Rethinking_Architecture_Design_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06047)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiangqiong\u002FViT-FL-main)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ae1CDi0_Nok&ab_channel=StanfordMedAI)] |\n| FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Singapore University of Technology and Design | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FXu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FXu_FedCorr_Multi-Stage_Federated_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04677)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxu-jingyi\u002Ffedcorr)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GA22ct1LgRA&ab_channel=ZihanChen)] |\n| FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Duke University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FTang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FTang_FedCor_Correlation-Based_Active_CVPR_2022_supplemental.zip)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2103.13822)] |\n| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FMa_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FMa_Layer-Wised_Model_Aggregation_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03993)] |\n| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FMendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FMendieta_Local_Learning_Matters_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14213)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmmendiet\u002FFedAlign)] |\n| Federated Learning With Position-Aware Neurons | Nanjing University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLi_Federated_Learning_With_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14666)] |\n| RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning | HKUST | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLiang_RSCFed_Random_Sampling_Consensus_Federated_Semi-Supervised_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLiang_RSCFed_Random_Sampling_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.13993)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxmed-lab\u002Frscfed)] |\n| Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wuhan University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FHuang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwenkehuang\u002Ffccl)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zZoASA71qwQ&ab_channel=HuangWenke)] |\n| Robust Federated Learning With Noisy and Heterogeneous Clients | Wuhan University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FFang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FFang_Robust_Federated_Learning_CVPR_2022_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FFangXiuwen\u002FRobust_FL)] |\n| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Arizona State University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLi_ResSFL_A_Resistance_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.04007)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlijingtao\u002FResSFL)] |\n| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FGao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11751)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaoliang13\u002FFedDC)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F505889549)] |\n| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FDong_Federated_Class-Incremental_Learning_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11473)] [[CODE](https:\u002F\u002Fgithub.com\u002FconditionWang\u002FFCIL)] |\n| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU;  JD Explore Academy;  The University of Sydney | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FZhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09249)] |\n| Differentially Private Federated Learning With Local Regularization and Sparsification | CAS | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FCheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03106)] |\n| Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage | University of Tennessee; Oak Ridge National Laboratory; Google Research | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.15696)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhuohangli\u002FGGL)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rphFSGDlGPY&ab_channel=MoSISLab)] |\n| CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | SJTU | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FShen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03880)] |\n| Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | Univ. of Pittsburgh; NVIDIA | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FXu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.10144)] |\n| Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | HHI | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FBecking_Adaptive_Differential_Filters_for_Fast_and_Communication-Efficient_Federated_Learning_CVPRW_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04424)] [[SILDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002FBecking_FSFL_FedVision_CVPR22.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FA9nEWqGriZ4)] |\n| MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients | Duke University | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FCao_MPAF_Model_Poisoning_Attacks_to_Federated_Learning_Based_on_Fake_CVPRW_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08669)] [[SILDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002Fmpaf.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FH3fetWD_ZHw)] |\n| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FWen_Communication-Efficient_Federated_Data_Augmentation_on_Non-IID_Data_CVPRW_2022_paper.html)] |\n| Does Federated Dropout Actually Work? | Stanford | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FCheng_Does_Federated_Dropout_Actually_Work_CVPRW_2022_paper.html)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FiJ3Q_gNhXGE)] |\n| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FLuo_FedIris_Towards_More_Accurate_and_Privacy-Preserving_Iris_Recognition_via_Federated_CVPRW_2022_paper.html)] [[SLIDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002Fpresentation-%20Zhengquan%20Luo.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FbRMeXncAjWY)] |\n| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Johns Hopkins University | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578476)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.02148)] [[CODE](https:\u002F\u002Fgithub.com\u002Fguopengf\u002FFL-MRCM)] |\n| Model-Contrastive Federated Learning :fire: | NUS; UC Berkeley | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578660)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16257)] [[CODE](https:\u002F\u002Fgithub.com\u002FQinbinLi\u002FMOON)] [[解读](https:\u002F\u002Fweisenhui.top\u002Fposts\u002F17666.html)] |\n| FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space :fire: | CUHK | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9577482)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06030)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliuquande\u002FFedDG-ELCFS)] |\n| Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Duke University | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578192)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06043)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjeremy313\u002FSoteria)] |\n| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710573)] |\n| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710586)] [[PDF](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGong_Ensemble_Attention_Distillation_for_Privacy-Preserving_Federated_Learning_ICCV_2021_paper.pdf)] |\n| Collaborative Unsupervised Visual Representation Learning from Decentralized Data | NTU; SenseTime | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710366)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06492)] |\n| Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification | NTU | MM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3474085.3475182)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06493)] |\n| Federated Visual Classification with Real-World Data Distribution | MIT; Google | ECCV | 2020 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-58607-2_5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.08082)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Rc67rZzPDDY&ab_channel=TzuMingHsu)] |\n| InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages |  | MM | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394171.3413923)] |\n| Performance Optimization of Federated Person Re-identification via Benchmark Analysis **`data.`** | NTU | MM | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394171.3413814)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.11560)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcap-ntu\u002FFedReID)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F265987079)] |\n\n\u003C!-- END:fl-in-top-cv-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n\n## fl in top nlp conference and journal\n\nFederated Learning papers accepted by top AI and NLP conference and journal, including [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Facl\u002Findex.html)(Annual Meeting of the Association for Computational Linguistics), [NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fnaacl\u002Findex.html)(North American Chapter of the Association for Computational Linguistics), [EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Femnlp\u002Findex.html)(Conference on Empirical Methods in Natural Language Processing) and [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fcoling\u002Findex.html)(International Conference on Computational Linguistics).\n\n- [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AACL%3A) [2025](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2025\u002F), [2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2024\u002F), [2023](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2023\u002F), [2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2022\u002F), [2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2021\u002F), [2019](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2019\u002F)\n- [NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANAACL-HLT%3A) [2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2024\u002F), [2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2022\u002F), [2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2021\u002F)\n- [EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AEMNLP%3A) [2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2024\u002F), [2023](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2023\u002F), [2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2022\u002F), [2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2021\u002F), [2020](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2020\u002F)\n- [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACOLING%3A) [2025](https:\u002F\u002Faclanthology.org\u002Fvolumes\u002F2025.coling-main\u002F), [2020](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fcoling-2020\u002F)\n\n\u003Cdetails open>\n\u003Csummary>fl in top nlp conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-nlp-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                          |    Venue             |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------- | -------------- | ---- | ------------------------------------------------------------ |\n| Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients |  | ACL | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.acl-long.19\u002F)] |\n| FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models |  | ACL | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.acl-long.67\u002F)] |\n| Federated Data-Efficient Instruction Tuning for Large Language Models |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.803\u002F)] |\n| FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.791\u002F)] |\n| Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.1241\u002F)] |\n| FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.733\u002F)] |\n| Gradient Inversion Attack in Federated Learning: Exposing Text Data through Discrete Optimization. |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.176\u002F)] |\n| FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models. |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.17\u002F)] |\n| Federated Incremental Named Entity Recognition. |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.13\u002F)] |\n| FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.581\u002F)] |\n| Federated Retrieval Augmented Generation for Multi-Product Question Answering |  | COLING (Industry) | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-industry.33\u002F)] |\n| A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-industry.64\u002F)] |\n| Safely Learning with Private Data: A Federated Learning Framework for Large Language Model |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.303)] |\n| FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.464)] |\n| Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.587)] |\n| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.717)] |\n| Promoting Data and Model Privacy in Federated Learning through Quantized LoRA |  | EMNLP Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-emnlp.615)] |\n| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models |  | EMNLP Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.717)] |\n| Generalizable Multilingual Hate Speech Detection on Low Resource Indian Languages using Fair Selection in Federated Learning |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.400\u002F)] |\n| Open-Vocabulary Federated Learning with Multimodal Prototyping |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.314\u002F)] |\n| Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.57\u002F)] |\n| FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering. |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.98\u002F)] |\n| Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning. |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.286\u002F)] |\n| Can Public Large Language Models Help Private Cross-device Federated Learning? |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.59\u002F)] |\n| Fair Federated Learning with Biased Vision-Language Models |  | ACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.595\u002F)] |\n| Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Auburn University | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.488\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15080)] [[CODE](https:\u002F\u002Fgithub.com\u002Fllm-eff\u002FFedPepTAO)] |\n| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | IIT Patna | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.999\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fappy1608\u002FEMNLP2023-Multimodal-Complaint-Detection)] |\n| FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models | YNU | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.529\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmaxinge8698\u002FFedID)] |\n| FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning | KAIST | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.734\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.16538)] |\n| Coordinated Replay Sample Selection for Continual Federated Learning | CMU | EMNLP industry Track | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-industry.32\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15054)] |\n| Tunable Soft Prompts are Messengers in Federated Learning | SYSU | EMNLP Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-emnlp.976\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.06805)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Ffedsp\u002Ffederatedscope\u002Fnlp\u002Ffedsp)] |\n| Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | OSU | ACL | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.678\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17221)] [[CODE](https:\u002F\u002Fgithub.com\u002Fosu-nlp-group\u002Ffl4semanticparsing)] |\n| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | HIT; Peng Cheng Lab | ACL | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.193\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedLegal)] |\n| Client-Customized Adaptation for Parameter-Efficient Federated Learning |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.75\u002F)] |\n| Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.327\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12449)] [[CODE](https:\u002F\u002Fgithub.com\u002Flancopku\u002Ffedmnmt)] |\n| Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.470\u002F)] |\n| FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.632\u002F)] |\n| Federated Learning of Gboard Language Models with Differential Privacy |  | ACL Industry Track | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-industry.60\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18465)] |\n| Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling | SNU | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.6\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.14017)] |\n| A Federated Approach to Predicting Emojis in Hindi Tweets | University of Alberta | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.819)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.06401)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdeep1401\u002Ffedmoji)] |\n| Federated Model Decomposition with Private Vocabulary for Text Classification | HIT; Peng Cheng Lab | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.430)] [[CODE](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedVocab)] |\n| Fair NLP Models with Differentially Private Text Encoders | Univ. Lille | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.514\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.06135)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsaist1993\u002Fdpnlp)] |\n| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.353)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06101)] [[CODE](https:\u002F\u002Fgithub.com\u002Fraipranav\u002Ffcl-fedseit)] |\n| Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation **`kg.`** | Lehigh University | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.43\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09553)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftaokz\u002FFedR)] |\n| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.25\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06894)] |\n| Scaling Language Model Size in Cross-Device Federated Learning | Google | ACL workshop | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.2\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.09715)] |\n| Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | Oxford | ACL workshop | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.4\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.02656)] |\n| ActPerFL: Active Personalized Federated Learning | Amazon | ACL workshop | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.1)] [[PAGE](https:\u002F\u002Fwww.amazon.science\u002Fpublications\u002Factperfl-active-personalized-federated-learning)] |\n| FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks :fire: | USC | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-naacl.13\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.08815)] [[CODE](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FFedNLP)] |\n| Federated Learning with Noisy User Feedback | USC; Amazon | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.196\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03092)] |\n| Training Mixed-Domain Translation Models via Federated Learning | Amazon | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.186)] [[PAGE](https:\u002F\u002Fwww.amazon.science\u002Fpublications\u002Ftraining-mixed-domain-translation-models-via-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.01557)] |\n| Pretrained Models for Multilingual Federated Learning | Johns Hopkins University | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.101)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02291)] [[CODE](https:\u002F\u002Fgithub.com\u002Forionw\u002Fmultilingual-federated-learning)] |\n| Federated Chinese Word Segmentation with Global Character Associations | University of Washington | ACL workshop | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.376)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuhksz-nlp\u002FGCASeg)] |\n| Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation | USTC | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.223)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05446)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyjw1029\u002FEfficient-FedRec)] [[VIDEO](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.223.mp4)] |\n| Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories | CUHK (Shenzhen) | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.321\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuhksz-nlp\u002FASA-TM)] [[VIDEO](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.321.mp4)] |\n| A Secure and Efficient Federated Learning Framework for NLP | University of Connecticut | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.606)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.11934)] [[VIDEO](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.606.mp4)] |\n| Distantly Supervised Relation Extraction in Federated Settings | UCAS | EMNLP workshop | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.52)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.05049)] [[CODE](https:\u002F\u002Fgithub.com\u002FDianboWork\u002FFedDS)] |\n| Federated Learning with Noisy User Feedback | USC; Amazon | NAACL workshop | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.196)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03092)] |\n| An Investigation towards Differentially Private Sequence Tagging in a Federated Framework | Universität Hamburg | NAACL workshop | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.privatenlp-1.4)] |\n| Understanding Unintended Memorization in Language Models Under Federated Learning | Google | NAACL workshop | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.privatenlp-1.1)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07490)] |\n| FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | CAS | EMNLP | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.165)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38939230)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F539347225)] |\n| Empirical Studies of Institutional Federated Learning For Natural Language Processing | Ping An Technology | EMNLP workshop | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.55)] |\n| Federated Learning for Spoken Language Understanding | PKU | COLING | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.310\u002F)] |\n| Two-stage Federated Phenotyping and Patient Representation Learning | Boston Children’s Hospital Harvard Medical School | ACL workshop | 2019 | [[PUB](https:\u002F\u002Faclanthology.org\u002FW19-5030)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.05596)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkaiyuanmifen\u002FFederatedNLP)] [[UC.](https:\u002F\u002Fgithub.com\u002FMarcioPorto\u002Ffederated-phenotyping)] |\n\n\u003C!-- END:fl-in-top-nlp-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n## fl in top ir conference and journal\n\nFederated Learning papers accepted by top Information Retrieval conference and journal, including [SIGIR](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fsigir\u002Findex.html)(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).\n\n- [SIGIR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGIR%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3726302), [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3626772), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3539618), [2022](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3477495), [2021](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3404835), [2020](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3397271)\n\n\u003Cdetails open>\n\u003Csummary>fl in top ir conference and journal\u003C\u002Fsummary>\n\n\u003C!-- START:fl-in-top-ir-conference-and-journal -->\n\n|Title                                                           |    Affiliation                        |    Venue    |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------- | ----- | ---- | ------------------------------------------------------------ |\n| FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation |  | SIGIR | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3729977)] |\n| NodeRec+: A Lightweight Framework for Federated Recommender Systems |  | SIGIR | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730138)] |\n| Unlearning for Federated Online Learning to Rank: A Reproducibility Study |  | SIGIR | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730336)] |\n| Joint Item Embedding Dual-view Exploration and Adaptive Local-Global Fusion for Federated Recommendation |  | SIGIR | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730016)] |\n| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | THU | SIGIR | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657763)] |\n| Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657764)] |\n| FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation | UQ | SIGIR | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657853)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.11891)] [[CODE](https:\u002F\u002Fgithub.com\u002Fielab\u002FFeB4RAG)] |\n| FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction | Alibaba Group | SIGIR | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657941)] |\n| Personalized Federated Relation Classification over Heterogeneous Texts | NUDT | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591748)] |\n| Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity | SDU | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591688)] |\n| Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures | UQ | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591722)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03054)] |\n| FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning | Alibaba Group | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591909)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.08328)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FElastic-Federated-Learning-Solution\u002Ftree\u002FFedAds)] |\n| Edge-cloud Collaborative Learning with Federated and Centralized Features (short-paper) | ZJU | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591976)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.05871)] |\n| FLIRT: Federated Learning for Information Retrieval (extended-abstract) | IMT Lucca | SIGIR | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591926)] |\n| Is Non-IID Data a Threat in Federated Online Learning to Rank? | The University of Queensland | SIGIR | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3477495.3531709)] [[CODE](https:\u002F\u002Fgithub.com\u002Fielab\u002F2022-SIGIR-noniid-foltr)] |\n| FedCT: Federated Collaborative Transfer for Recommendation | Rutgers University | SIGIR | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462825)] [[PDF](http:\u002F\u002Fyongfeng.me\u002Fattach\u002Fliu-sigir2021.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FCharlieMat\u002FEdgeCDR)] |\n| On the Privacy of Federated Pipelines | Technical University of Munich | SIGIR | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462996)] |\n| FedCMR: Federated Cross-Modal Retrieval. | Dalian University of Technology | SIGIR | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462989)] [[CODE](https:\u002F\u002Fgithub.com\u002FhasakiXie123\u002FFedCMR)] |\n| Meta Matrix Factorization for Federated Rating Predictions. | SDU | SIGIR | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3397271.3401081)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.10086)] |\n\n\u003C!-- END:fl-in-top-ir-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n## fl in top db conference and journal\n\nFederated Learning papers accepted by top Database conference and journal, including [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fsigmod\u002Findex.html)(ACM SIGMOD Conference) , [ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficde\u002Findex.html)(IEEE International Conference on Data Engineering) and [VLDB](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fvldb\u002Findex.html)(Very Large Data Bases Conference).\n\n- [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsigmod%3A) [2022](https:\u002F\u002F2022.sigmod.org\u002Fsigmod_research_list.shtml), [2021](https:\u002F\u002F2021.sigmod.org\u002Fsigmod_research_list.shtml)\n- [ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICDE%3A) [2025](https:\u002F\u002Fieee-icde.org\u002F2025\u002Fresearch-papers\u002F), [2024](https:\u002F\u002Ficde2024.github.io\u002F), [2023](https:\u002F\u002Ficde2023.ics.uci.edu\u002Fpapers-research-track\u002F), [2022](https:\u002F\u002Ficde2022.ieeecomputer.my\u002Faccepted-research-track\u002F), [2021](https:\u002F\u002Fieeexplore.ieee.org\u002Fxpl\u002Fconhome\u002F9458599\u002Fproceeding)\n- [VLDB](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpvldb%3A) [2025](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F18), [2024](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [2023](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [2022](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol16-volume-info\u002F), [2021](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol15-volume-info\u002F), [2021](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002F), [2020](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13-volume-info\u002F)\n\n\u003Cdetails open>\n\u003Csummary>fl in top db conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-db-conference-and-journal -->\n\n|Title                                                        | Affiliation                     | Venue           | Year | Materials|\n| ------------------------------------------------------------ | ------------------------------- | --------------- | ---- | ------------------------------------------------------------ |\n| PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748215)] [[CODE](https:\u002F\u002Fgithub.com\u002FZhouXiaokay\u002FVF-SV)] |\n| Federated Incomplete Tabular Data Prediction with Missing Complementarity |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748213)] [[CODE](https:\u002F\u002Fgithub.com\u002FLS5221\u002FDARN)] |\n| Federated and Balanced Clustering for High-Dimensional Data |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3749646.3749673)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwhu-totemdb\u002FTeb-means)] |\n| FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3750601.3750674)] |\n| GORAM: Graph-Oriented ORAM for Efficient Ego-Centric Queries on Federated Graphs |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748218)] [[CODE](https:\u002F\u002Fgithub.com\u002FFannxy\u002FGORAM-ABY3)] |\n| Federated Data Distribution Shift Estimation |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3742728.3742736)] [[CODE](https:\u002F\u002Ffigshare.com\u002Fs\u002F7a1c725a293d1c5b88a8)] |\n| OpenFGL: A Comprehensive Benchmark for Federated Graph Learning |  | VLDB | 2025 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol18\u002Fp1305-li.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FxkLi-Allen\u002FOpenFGL)] |\n| A Bargaining-Based Approach for Feature Trading in Vertical Federated Learning |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113094)] |\n| pFSSL-D: Generalization Meets Personalization in Dual-Phase Federated Semi-Supervised Learning |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113223)] |\n| FedEcover: Fast and Stable Converging Model-Heterogeneous Federated Learning with Efficient-Coverage Submodel Extraction |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113012)] |\n| Federated Trajectory Similarity Learning with Privacy-Preserving Clustering |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113111)] |\n| FedSDP: Federated Self-Derived Prototypes for Personalized Federated Learning |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112874)] |\n| Federated Data Analytics with Differentially Private Density Estimation Model. |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112917)] |\n| Efficient Data Valuation Approximation in Federated Learning: A Sampling-Based Approach. |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112901)] |\n| pFedAFM: Adaptive Feature Mixture for Data-Level Personalization in Heterogeneous Federated Learning on Mobile Edge Devices. |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113035)] |\n| Heterogeneous-Aware Traffic Prediction: A Privacy-Preserving Federated Learning Framework. |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113014)] |\n| Online Federated Learning on Distributed Unknown Data Using UAVs |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112962)] |\n| Hounding Data Diversity: Towards Participant Selection in Vertical Federated Learning. |  | ICDE | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113194)] |\n| FedMix: Boosting with Data Mixture for Vertical Federated Learning |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597834)] |\n| FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597740)] |\n| Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598007)] |\n| Semi-Asynchronous Online Federated Crowdsourcing |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598143)] |\n| AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597891)] |\n| MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597905)] |\n| LightTR: A Lightweight Framework for Federated Trajectory Recovery |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598171)] |\n| Feed: Towards Personalization-Effective Federated Learning |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597724)] |\n| Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597841)] |\n| Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597940)] |\n| HeteFedRec: Federated Recommender Systems with Model Heterogeneity |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598074)] |\n| Hide Your Model: A Parameter Transmission-free Federated Recommender System |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597708)] |\n| FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598064)] |\n| Preventing the Popular Item Embedding Based Attack in Federated Recommendations |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597721)] |\n| RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection |  | ICDE | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597878)] |\n| Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly | TUM | DEEM@SIGMOD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3650203.3663331)] |\n| FedSQ: A Secure System for Federated Vector Similarity Queries |  | VLDB | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685895)] |\n| FedSM: A Practical Federated Shared Mobility System |  | VLDB | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685896)] |\n| OFL-W3: A One-Shot Federated Learning System on Web 3.0 |  | VLDB | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685900)] |\n| Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation |  | VLDB | 2024 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp2077-li.pdf)] |\n| Uldp-FL: Federated Learning with Across Silo User-Level Differential Privacy. |  | VLDB | 2024 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp2826-kato.pdf)] |\n| Performance-Based Pricing of Federated Learning via Auction | Alibaba Group | VLDB | 2024 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FPerformance-Based%20Pricing%20of%20Federated%20Learning%20via%20Auction)] [[CODE](https:\u002F\u002Fgithub.com\u002FZiTao-Li\u002Ffl_auction)] |\n| A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer | NJU | VLDB | 2024 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FA%20Blockchain%20System%20for%20Clustered%20Federated%20Learning%20with%20Peer-to-Peer%20Knowledge%20Transfer)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnju-websoft\u002FFedChain)] |\n| Communication Efficient and Provable Federated Unlearning | SDU; KAUST | VLDB | 2024 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FCommunication%20Efficient%20and%20Provable%20Federated%20Unlearning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.11018)] [[CODE](https:\u002F\u002Fgithub.com\u002FHappy2Git\u002FFATS_supplement)] |\n| Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices | USTC | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184749)] |\n| Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs | Columbia University | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184557)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdongzizhu\u002FFedDA)] |\n| FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | BIT | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184531)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01738)] |\n| Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices | SJTU | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184796)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.00492)] |\n| Federated IoT Interaction Vulnerability Analysis | MSU | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184681)] |\n| Distribution-Regularized Federated Learning on Non-IID Data | BUAA | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184650)] |\n| Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data | ShanghaiTech University | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184550)] [[CODE](https:\u002F\u002Fgithub.com\u002FSongjieXie\u002FFed-SC)] |\n| FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration | ZJU | ICDE | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184883)] |\n| FedGTA: Topology-aware Averaging for Federated Graph Learning. | BIT | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp41-li.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FxkLi-Allen\u002FFedGTA)] |\n| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Alibaba Group | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp4046-chen.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13363)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002FFSreal)] |\n| Federated Calibration and Evaluation of Binary Classifiers. | meta | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp3253-cormode.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.12526)] [[CODE](https:\u002F\u002Ffigshare.com\u002Fs\u002F607998e479b0778645f6)] |\n| Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Kyoto University | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp2404-kato.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.07165)] [[CODE](https:\u002F\u002Fgithub.com\u002FFumiyukiKato\u002FFL-TEE)] |\n| Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | NUS | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp2471-ooi.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnusdbsystem\u002Ffalcon)] |\n| Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1277-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01700)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002Fpublic_vflclustering-63CD\u002FREADME.md)] |\n| FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. :fire: | Alibaba | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1059-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.05011)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope)] |\n| Secure Shapley Value for Cross-Silo Federated Learning. | Kyoto University | VLDB | 2023 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1657-zheng.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04856)] [[CODE](https:\u002F\u002Fgithub.com\u002Fteijyogen\u002Fsecsv)] |\n| OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | ZJU | VLDB | 2022 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp202-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01318)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba-edu\u002Fmpc4j\u002Ftree\u002Fmain\u002Fmpc4j-sml-opboost)] |\n| Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. | NUS | VLDB | 2022 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp2348-bao.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FSkellamMixtureMechanism\u002FSMM)] |\n| Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update | PKU | VLDB | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3547305.3547316)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.14628)] [[CODE](https:\u002F\u002Fgithub.com\u002Fccchengff\u002FFDL\u002Ftree\u002Fmain\u002Fplayground\u002Fcelu_vfl)] |\n| FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification. | HIT | VLDB | 2022 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp3686-wang.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhit-mdc\u002FFedTSC-FedST)] |\n| Improving Fairness for Data Valuation in Horizontal Federated Learning | The UBC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835382)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09046)] |\n| FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity | USTC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835545)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03529)] [[CODE](https:\u002F\u002Fgithub.com\u002FYonghaiGong\u002FFedADMM)] |\n| FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. | USTC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835327)] |\n| Federated Learning on Non-IID Data Silos: An Experimental Study. :fire: | NUS | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835537)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.02079)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FNIID-Bench)] |\n| Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing | USTC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835657)] |\n| Samba: A System for Secure Federated Multi-Armed Bandits | Univ. Clermont Auvergne | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835585)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgamarcad\u002Fsamba-demo)] |\n| FedRecAttack: Model Poisoning Attack to Federated Recommendation | ZJU | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835228)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.01499)] [[CODE](https:\u002F\u002Fgithub.com\u002Frdz98\u002Ffedrecattack)] |\n| Enhancing Federated Learning with In-Cloud Unlabeled Data | USTC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835163)] |\n| Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835159)] |\n| An Introduction to Federated Computation | University of Warwick; Facebook | SIGMOD Tutorial | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3514221.3522561)] |\n| BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; Tencent | SIGMOD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3514221.3526127)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07975)] |\n| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458704)] [[RELATED PAPER(ZH)](https:\u002F\u002Fkns.cnki.net\u002Fkcms\u002Fdetail\u002Fdetail.aspx?doi=10.13328\u002Fj.cnki.jos.006174)] |\n| Feature Inference Attack on Model Predictions in Vertical Federated Learning | NUS | ICDE | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458672\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.10152)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxj231\u002Ffeatureinference-vfl)] |\n| Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458829)] |\n| Federated Matrix Factorization with Privacy Guarantee | Purdue | VLDB | 2021 | [[PUB](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp900-li.pdf)] |\n| Projected Federated Averaging with Heterogeneous Differential Privacy. | Renmin University of China | VLDB | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3503585.3503592)] [[CODE](https:\u002F\u002Fgithub.com\u002FEmory-AIMS\u002FPFA)] |\n| Enabling SQL-based Training Data Debugging for Federated Learning | Simon Fraser University | VLDB | 2021 | [[PUB](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp388-wu.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.11884)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsfu-db\u002FFedRain-and-Frog)] |\n| Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | ZJU | VLDB | 2021 | [[PUB](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol14\u002Fp2659-jiang.pdf)] |\n| Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks | Tanium Inc. | VLDB | 2021 | [[PUB](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002Fp3096-stoddard.pdf)] [[VIDEO](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Wg411j7aA)] |\n| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | PKU | SIGMOD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3448016.3457241)] |\n| ExDRa: Exploratory Data Science on Federated Raw Data | SIEMENS | SIGMOD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3448016.3457549)] |\n| Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460866.3461765)] |\n| Privacy Preserving Vertical Federated Learning for Tree-based Models | NUS | VLDB | 2020 | [[PUB](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13\u002Fp2090-wu.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06170)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sjii8oVCqiY)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnusdbsystem\u002Fpivot)] |\n\n\u003C!-- END:fl-in-top-db-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n## fl in top network conference and journal\n\n\nFederated Learning papers accepted by top Database conference and journal, including [SIGCOMM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fsigcomm\u002Findex.html)(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), [INFOCOM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Finfocom\u002Findex.html)(IEEE Conference on Computer Communications), [MobiCom](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fmobicom\u002Findex.html)(ACM\u002FIEEE International Conference on Mobile Computing and Networking), [NSDI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fnsdi\u002Findex.html)(Symposium on Networked Systems Design and Implementation) and [WWW](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fwww\u002Findex.html)(The Web Conference).\n\n- [SIGCOMM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGCOMM%3A) 2025\n- [INFOCOM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AINFOCOM%3A) [2025](https:\u002F\u002Finfocom2025.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2024](https:\u002F\u002Finfocom2024.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2023](https:\u002F\u002Finfocom2023.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2022](https:\u002F\u002Finfocom2022.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference)([Page](https:\u002F\u002Finfocom.info\u002Fday\u002F3\u002Ftrack\u002FTrack%20B#B-7)), [2021](https:\u002F\u002Finfocom2021.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html)([Page](https:\u002F\u002Fduetone.org\u002Finfocom21)), [2020](https:\u002F\u002Finfocom2020.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html)([Page](https:\u002F\u002Fduetone.org\u002Finfocom20)), [2019](https:\u002F\u002Finfocom2019.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), 2018\n- [MobiCom](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AMobiCom%3A) [2024](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2024\u002Faccepted.html), [2023](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2023\u002Faccepted.html), [2022](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2022\u002Faccepted.html), [2021](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2021\u002Faccepted.html), [2020](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2020\u002Faccepted.php)\n- [NSDI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANSDI%3A) [2025](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Ftechnical-sessions), 2023([Spring](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Fspring-accepted-papers), [Fall](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Ffall-accepted-papers))\n- [WWW](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWWW%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3696410), [2024](https:\u002F\u002Fwww2024.thewebconf.org\u002Faccepted\u002Fresearch-tracks\u002F), [2023](https:\u002F\u002Fwww2023.thewebconf.org\u002Fprogram\u002Faccepted-papers\u002F), [2022](https:\u002F\u002Fwww2022.thewebconf.org\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww2021.thewebconf.org\u002Fprogram\u002Fpapers-program\u002Flinks\u002Findex.html)\n\n\u003Cdetails open>\n\u003Csummary>fl in top network conference and journal\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-network-conference-and-journal -->\n\n|Title                                                           |    Affiliation                                                     |    Venue         |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | ---- | ------------------------------------------------------------ |\n| A Lightweight Emulation Framework for Energy-Aware Federated Learning |  | SIGCOMM (Posters and Demos) | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748395)] |\n| NEBULA - Decentralized Federated Learning for Heterogeneous Networks |  | SIGCOMM (Posters and Demos) | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748413)] |\n| Federated Inference: Towards Collaborative and Privacy-Preserving Inference over Edge Devices |  | SIGCOMM (Posters and Demos) | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748418)] |\n| Preference Profiling Attacks Against Vertical Federated Learning Over Graph Data |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044459)] |\n| FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044589)] |\n| Federated Adaptive Fine-Tuning of Large Language Models with Heterogeneous Quantization and LoRA |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044641)] |\n| FLM-TopK: Expediting Federated Large Language Model Tuning by Sparsifying Intervalized Gradients |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044514)] |\n| Client Sampling for Communication-Efficient Distributed Minimax Optimization |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044637)] |\n| Robust Contextual Combinatorial Multi-Armed Bandits for Unreliable Network Systems |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044618)] |\n| ElasticFed: Collaborative Large-Small Transformer Training for Federated Continual Learning at Edge |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044503)] |\n| γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044558)] |\n| PSFL: Parallel-Sequential Federated Learning with Convergence Guarantees |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044534)] |\n| GraphRx: Graph-Based Collaborative Learning Among Multiple Cells for Uplink Neural Receivers |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044726)] |\n| Input Integrity and Authentic Results: Towards Trustworthy Aggregation in Federated Learning |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044719)] |\n| Lightweight Federated Learning with Differential Privacy and Straggler Resilience |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044562)] |\n| Communication Efficient Asynchronous Stochastic Gradient Descent |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044686)] |\n| GeoFL: A Framework for Efficient Geo-Distributed Cross-Device Federated Learning |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044713)] |\n| FedPDA: Collaborative Learning for Reducing Online-Adaptation Frequency of Neural Receivers |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044747)] |\n| LCO-AGQ: A Lightweight Client-Oriented Adaptive Gradient Quantization Algorithm for Federated Learning |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044636)] |\n| FedEXT: Differential Federated Learning with Complementary Extension of Edge Models |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044645)] |\n| FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044717)] |\n| Similarity-Guided Rapid Deployment of Federated Intelligence Over Heterogeneous Edge Computing |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044586)] |\n| CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044535)] |\n| Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044570)] |\n| Multi-Task Reinforcement Learning for Collaborative Network Optimization in Data Centers |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044699)] |\n| Towards Federated Inference: An Online Model Ensemble Framework for Cooperative Edge AI |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044578)] |\n| VaniKG: Vanishing Key Gradient Attack and Defense for Robust Federated Aggregation |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044620)] |\n| Combating Deep Leakage from Gradients in Cross-Silo Federated Learning with QKD |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044743)] |\n| FedUFD: Personalized Edge Computing Using Federated Uncertainty-Driven Feature Distillation |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044664)] |\n| Accelerating Clustered Federated Learning in Dynamic D2D Networks with Transferable GNN |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044690)] |\n| AoI-Aware Federated Unlearning for Streaming Data with Online Client Selection and Pricing |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044760)] |\n| Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714911)] |\n| Empowering Federated Graph Rationale Learning with Latent Environments |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714929)] |\n| Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems against Attribute Inference Attacks |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714823)] |\n| P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714721)] |\n| Local Differentially Private Release of Infinite Streams With Temporal Relevance |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714619)] |\n| Unlearning Incentivizes Learning under Privacy Risk |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714740)] |\n| Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714884)] |\n| Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714722)] |\n| Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714961)] |\n| Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714703)] |\n| Federated Graph Anomaly Detection via Disentangled Representation Learning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714567)] |\n| FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714623)] |\n| Subgraph Federated Unlearning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714821)] |\n| Personalized Federated Recommendation for Cold-Start Users via Adaptive Knowledge Fusion |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714635)] |\n| NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714630)] |\n| FedRIR: Rethinking Information Representation in Federated Learning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714612)] |\n| PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714561)] |\n| Provably Robust Federated Reinforcement Learning |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714728)] |\n| MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714515)] |\n| FLock: Robust and Privacy-Preserving Federated Learning based on Practical Blockchain State Channels |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714666)] |\n| PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality |  | NSDI | 2025 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Fpresentation\u002Fsrinivas)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fnsdi25_slides-srinivas.pdf)] |\n| Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621090)] |\n| Strategic Data Revocation in Federated Unlearning |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621201)] |\n| FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621176)] |\n| Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621105)] |\n| FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621438)] |\n| DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service |  | INFOCOM | 2024 | [[PUB](https:","# 联邦学习资源\n\n[![星标](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fyoungfish42\u002FAwesome-FL.svg?color=orange)](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fstargazers) [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge-flat.svg)](https:\u002F\u002Fawesome.re) [![许可证](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fyoungfish42\u002FAwesome-FL.svg?color=green)](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002Fimage-registration-resources\u002Fblob\u002Fmaster\u002FLICENSE) ![](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002Fyoungfish42\u002FAwesome-FL) \n\n---\n\n**目录**\n\n- [论文](#papers)\n  - [顶级期刊中的联邦学习](#fl-in-top-tier-journal)\n  - 按类别划分的顶级会议和期刊中的联邦学习\n    - [人工智能](#fl-in-top-ai-conference-and-journal) [机器学习](#fl-in-top-ml-conference-and-journal) [数据挖掘](#fl-in-top-dm-conference-and-journal) [安全](#fl-in-top-secure-conference-and-journal) [计算机视觉](#fl-in-top-cv-conference-and-journal) [自然语言处理](#fl-in-top-nlp-conference-and-journal) [信息检索](#fl-in-top-ir-conference-and-journal) [数据库](#fl-in-top-db-conference-and-journal) [网络](#fl-in-top-network-conference-and-journal) [系统](#fl-in-top-system-conference-and-journal) [其他](#fl-in-top-conference-and-journal-other-fields)\n  - [图数据与图神经网络上的联邦学习](#fl-on-graph-data-and-graph-neural-networks) [![dblp](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A%2F%2Fdblp.org%2Fsearch%2Fpubl%2Fapi%3Fq%3DFederated%2520graph%257Csubgraph%257Cgnn%26format%3Djson%26h%3D1000)](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=Federated%20graph%7Csubgraph%7Cgnn) \n  - [表格数据上的联邦学习](#fl-on-tabular-data) [![dblp](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A\u002F\u002Fdblp.org\u002Fsearch\u002Fpubl\u002Fapi%3Fq%3Dfederate%2520tree%257Cboost%257Cbagging%257Cgbdt%257Ctabular%257Cforest%257CXGBoost%26format%3Djson%26h%3D1000)](https:\u002F\u002Fdblp.org\u002Fsearch?q=federate%20tree%7Cboost%7Cbagging%7Cgbdt%7Ctabular%7Cforest%7CXGBoost)\n- [框架](#framework)\n- [数据集](#datasets)\n- [综述](#surveys)\n- [教程和课程](#tutorials-and-courses)\n- 重要会议\u002F研讨会\u002F期刊\n  - [研讨会](#workshops) [特刊](#journal-special-issues) [特别专题](#conference-special-tracks)\n- [更新日志](#update-log)\n- [致谢](#acknowledgments)\n- [引用](#citation)\n\n\n\n我们使用另一个项目来自动跟踪联邦学习论文的更新，如果您需要，请点击 [FL-paper-update-tracker](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FFL-paper-update-tracker)。\n\n请注意，如果此页面未显示全部内容，请 **访问[官方主页](https:\u002F\u002Fyoungfish42.github.io\u002FAwesome-FL)以获取完整信息。**\n\n**将会有更多条目被添加到仓库中**。请随时通过提交 [issue](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues) 报告、提交 pull request 或发送电子邮件至 ([im.young@foxmail.com](mailto:im.young@foxmail.com)) 建议其他关键资源。如果您想与更多联邦学习领域的同行交流，请加入 QQ 群 [联邦学习交流群]，群号为 833638275。祝您阅读愉快！\n\n\n\n**仓库更新通知**\n\n> 2024年9月30日\n>\n> \n>\n> 尊敬的用户们，我们在此告知您一些将影响本开源仓库的变化。仓库的所有者兼主要贡献者 [@youngfish42](https:\u002F\u002Fgithub.com\u002Fyoungfish42) 已于2024年9月30日顺利完成博士学业 🎓，此后已调整其研究方向。这一变化将影响仓库论文列表的更新频率和深度。\n>\n> 相较于以往的定期更新，我们预计今后论文列表将改为每月或每季度更新一次。此外，更新的内容也将有所减少。例如，关于作者所在机构及开源代码的相关信息将不再持续维护。\n>\n> 我们理解这可能会对您从本仓库中获得的价值产生一定影响。因此，我们诚挚地邀请更多贡献者参与内容的更新工作。通过大家的共同努力，我们将确保该仓库继续成为一项宝贵的资源。\n>\n> 感谢您的理解，并期待您一如既往的支持与贡献。\n>\n> \n>\n> 此致敬礼，\n>\n> 白小鱼 (youngfish)\n>\n\n\n\n\n# 论文\n\n**分类**\n\n- 人工智能（IJCAI、AAAI、AISTATS、ALT、AI）\n\n- 机器学习（NeurIPS、ICML、ICLR、COLT、UAI、Machine Learning、JMLR、TPAMI）\n\n- 数据挖掘（KDD、WSDM）\n\n- 安全（S&P、CCS、USENIX Security、NDSS）\n\n- 计算机视觉（ICCV、CVPR、ECCV、MM、IJCV）\n\n- 自然语言处理（ACL、EMNLP、NAACL、COLING）\n\n- 信息检索（SIGIR）\n\n- 数据库（SIGMOD、ICDE、VLDB）\n\n- 网络（SIGCOMM、INFOCOM、MOBICOM、NSDI、WWW）\n\n- 系统（OSDI、SOSP、ISCA、MLSys、EuroSys、TPDS、DAC、TOCS、TOS、TCAD、TC） \n\n- 其他（ICSE、FOCS、STOC）\n\n\n\n\n\u003Cdetails open>\n\u003Csummary> 活动 \u003C\u002Fsummary>\n\n| 场所                                                        | 2024-2020                                                    | 2020年之前                                                  |\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |\n| [IJCAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AIJCAI%3A) | [25](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F), [24](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F), [23](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F), [22](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F), [21](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F), [20](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F) | [19](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F)                |\n| [AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAAAI%3A) | [25](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2025.html), [24](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2024.html), [23](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2023), [22](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-22\u002Fwp-content\u002Fuploads\u002F2021\u002F12\u002FAAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [21](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-21\u002Fwp-content\u002Fuploads\u002F2020\u002F12\u002FAAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [20](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FAAAI-20-Accepted-Paper-List.pdf) | -                                                            |\n| [AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAISTATS%3A) | [25](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002F), [24](http:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002F), [23](http:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002F), [22](http:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002F), [21](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002F), [20](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002F) | -                                                            |\n| [ALT](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Falt%3A) | 22                                                           | -                                                            |\n| [AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fai%3A) (J) | 25, 23                                                       | -                                                            |\n| [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANeurIPS%3A) | [24](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2024\u002FConference#tab-accept-oral), [23](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2023\u002FConference#tab-accept-oral), [22](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2022), [21](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021), [20](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020) | [18](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018), [17](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F17) |\n| [ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICML%3A) | [25](https:\u002F\u002Ficml.cc\u002FConferences\u002F2025\u002FSchedule?type=Poster), [24](https:\u002F\u002Ficml.cc\u002FConferences\u002F2024\u002FSchedule?type=Poster), [23](https:\u002F\u002Ficml.cc\u002FConferences\u002F2023\u002FSchedule?type=Poster), [22](https:\u002F\u002Ficml.cc\u002FConferences\u002F2022\u002FSchedule?type=Poster), [21](https:\u002F\u002Ficml.cc\u002FConferences\u002F2021\u002FSchedule?type=Poster), [20](https:\u002F\u002Ficml.cc\u002FConferences\u002F2020\u002FSchedule?type=Poster) | [19](https:\u002F\u002Ficml.cc\u002FConferences\u002F2019\u002FSchedule?type=Poster)  |\n| [ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICLR%3A) | [25](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2025), [24](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2024\u002FConference), [23](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2023\u002FConference), [22](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2022\u002FConference), [21](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2021\u002FConference), [20](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2020\u002FConference) | -                                                            |\n| [COLT](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ACOLT%3A) | [23](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002F)                    | -                                                            |\n| [UAI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AUAI%3A)  | [25](https:\u002F\u002Fwww.auai.org\u002Fuai2025\u002Faccepted_papers), [24](https:\u002F\u002Fwww.auai.org\u002Fuai2024\u002Faccepted_papers), [23](https:\u002F\u002Fwww.auai.org\u002Fuai2023\u002Faccepted_papers), [22](https:\u002F\u002Fwww.auai.org\u002Fuai2022\u002Faccepted_papers), [21](https:\u002F\u002Fwww.auai.org\u002Fuai2021\u002Faccepted_papers) | -                                                            |\n| [Machine Learning](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fml%3A) (J) | 25, 24, 23, 22                                               | -                                                            |\n| [JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fjmlr%3A) (J) | 24, 23, 22                                                   | -                                                            |\n| [TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpami%3A) (J) | 25, 24, 23, 22                                               | -                                                            |\n| [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AKDD%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3690624), [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3637528), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3580305), [22](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperRT.html), [21](https:\u002F\u002Fkdd.org\u002Fkdd2021\u002Faccepted-papers\u002Findex), [20](https:\u002F\u002Fwww.kdd.org\u002Fkdd2020\u002Faccepted-papers) |                                                              |\n| [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWSDM%3A) | [25](https:\u002F\u002Fwww.wsdm-conference.org\u002F2025\u002Faccepted-papers\u002F), [24](https:\u002F\u002Fwww.wsdm-conference.org\u002F2024\u002Faccepted-papers\u002F), [23](https:\u002F\u002Fwww.wsdm-conference.org\u002F2023\u002Fprogram\u002Faccepted-papers), [22](https:\u002F\u002Fwww.wsdm-conference.org\u002F2022\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww.wsdm-conference.org\u002F2021\u002Faccepted-papers.php) | [19](https:\u002F\u002Fwww.wsdm-conference.org\u002F2019\u002Faccepted-papers.php) |\n| [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsp%3A) | [25](https:\u002F\u002Fsp2025.ieee-security.org\u002Fprogram-papers.html), [24](https:\u002F\u002Fsp2024.ieee-security.org\u002Fprogram-papers.html), [23](https:\u002F\u002Fsp2023.ieee-security.org\u002Fprogram-papers.html), [22](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2022\u002Fprogram-papers.html) | [19](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002Fprogram-papers.html) |\n| [CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACCS%3A) | [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3658644), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3576915), [22](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2022\u002Fprogram\u002Faccepted-papers.html), [21](https:\u002F\u002Fsigsac.org\u002Fccs\u002FCCS2021\u002Faccepted-papers.html), [19](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2019\u002Findex.php\u002Fprogram\u002Faccepted-papers\u002F) | [17](https:\u002F\u002Facmccs.github.io\u002Fpapers\u002F)                       |\n| [USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fuss%3A) | [23](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Ftechnical-sessions), [22](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Ftechnical-sessions), [20](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Ftechnical-sessions) | -                                                            |\n| [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANDSS%3A) | [25](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2025\u002Faccepted-papers\u002F), [24](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2024\u002Faccepted-papers\u002F), [23](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2023\u002Faccepted-papers\u002F), [22](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2022\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2021\u002Faccepted-papers\u002F) | -                                                            |\n| [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACVPR%3A) | [25](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2025?day=all), [24](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2024?day=all), [23](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2023?day=all), [22](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2022), [21](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2021?day=all) | -                                                            |\n| [ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICCV%3A) | [23](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2023?day=all),[21](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2021?day=all) | -                                                            |\n| [ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AECCV%3A) | [24](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [22](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php), [20](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php) | -                                                            |\n| [MM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fmm%3A) | [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3664647), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3581783), [22](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fmm\u002Fmm2022.html), [21](https:\u002F\u002F2021.acmmm.org\u002Fmain-track-list), [20](https:\u002F\u002F2020.acmmm.org\u002Fmain-track-list.html) | -                                                            |\n| [IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fijcv%3A) (J) | 25, 24                                                       | -                                                            |\n| [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AACL%3A) | [25](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2025\u002F), [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2024\u002F), [23](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2023\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2021\u002F) | [19](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2019\u002F)              |\n| [NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANAACL-HLT%3A) | [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2024\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2021\u002F) | -                                                            |\n| [EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AEMNLP%3A) | [24](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2024\u002F), [23](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2023\u002F), [22](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2022\u002F), [21](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2021\u002F), [20](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2020\u002F) | -                                                            |\n| [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACOLING%3A) | [25](https:\u002F\u002Faclanthology.org\u002Fvolumes\u002F2025.coling-main\u002F), [20](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fcoling-2020\u002F) | -                                                            |\n| [SIGIR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGIR%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3726302), [24](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3626772), [23](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3539618), [22](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3477495), [21](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3404835), [20](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3397271) | -                                                            |\n| [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsigmod%3A) | [22](https:\u002F\u002F2022.sigmod.org\u002Fsigmod_research_list.shtml), [21](https:\u002F\u002F2021.sigmod.org\u002Fsigmod_research_list.shtml) | -                                                            |\n| [ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICDE%3A) | [25](https:\u002F\u002Fieee-icde.org\u002F2025\u002Fresearch-papers\u002F), [24](https:\u002F\u002Ficde2024.github.io\u002F), [23](https:\u002F\u002Ficde2023.ics.uci.edu\u002Fpapers-research-track\u002F), [22](https:\u002F\u002Ficde2022.ieeecomputer.my\u002Faccepted-research-track\u002F), [21](https:\u002F\u002Fieeexplore.ieee.org\u002Fxpl\u002Fconhome\u002F9458599\u002Fproceeding) | -                                                            |\n| [VLDB](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | [25](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F18), [24](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [23](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [22](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol16-volume-info\u002F), [21](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol15-volume-info\u002F), [21](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002F), [20](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13-volume-info\u002F) | -                                                            |\n| [SIGCOMM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGCOMM%3A) | 25                                                           | -                                                            |\n| [INFOCOM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AINFOCOM%3A) | [25](https:\u002F\u002Finfocom2025.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [24](https:\u002F\u002Finfocom2024.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [23](https:\u002F\u002Finfocom2023.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [22](https:\u002F\u002Finfocom2022.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [21](https:\u002F\u002Finfocom2021.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), [20](https:\u002F\u002Finfocom2020.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html) | [19](https:\u002F\u002Finfocom2019.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), 18 |\n| [MobiCom](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AMobiCom%3A) | [24](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2024\u002Faccepted.html), [23](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2023\u002Faccepted.html), [22](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2022\u002Faccepted.html), [21](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2021\u002Faccepted.html), [20](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2020\u002Faccepted.php) |                                                              |\n| [NSDI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANSDI%3A) | [25](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Ftechnical-sessions), 23([1](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Fspring-accepted-papers), [2](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Ffall-accepted-papers)) | -                                                            |\n| [WWW](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWWW%3A) | [25](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3696410), [24](https:\u002F\u002Fwww2024.thewebconf.org\u002Faccepted\u002Fresearch-tracks\u002F), [23](https:\u002F\u002Fwww2023.thewebconf.org\u002Fprogram\u002Faccepted-papers\u002F), [22](https:\u002F\u002Fwww2022.thewebconf.org\u002Faccepted-papers\u002F), [21](https:\u002F\u002Fwww2021.thewebconf.org\u002Fprogram\u002Fpapers-program\u002Flinks\u002Findex.html) |                                                              |\n| [OSDI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AOSDI%3A) | 21                                                           | -                                                            |\n| [SOSP](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ASOSP%3A) | 21                                                           | -                                                            |\n| [ISCA](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AISCA%3A) | [24](https:\u002F\u002Fwww.iscaconf.org\u002Fisca2024\u002Fprogram\u002F)             | -                                                            |\n| [MLSys](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AMLSys%3A) | [24](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2024), [23](https:\u002F\u002Fproceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2023), [22](proceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2022), [20](proceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2020) | [19](proceedings.mlsys.org\u002Fpaper_files\u002Fpaper\u002F2019)   |\n| [EuroSys](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Feurosys%3A) | [25](https:\u002F\u002F2025.eurosys.org\u002Faccepted-papers.html), [24](https:\u002F\u002F2024.eurosys.org\u002Faccepted-papers.html), [23](https:\u002F\u002F2023.eurosys.org\u002Faccepted-papers.html), 22, 21, 20 |                                                              |\n| [TPDS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Ftpds%3A) (J) | 25, 24, 23, 22, 21, 20                                       | -                                                            |\n| [DAC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ADAC%3A) | 25, 24, 22, 21                                               | -                                                            |\n| [TOCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftocs%3A) | -                                                            | -                                                            |\n| [TOS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftos%3A) | -                                                            | -                                                            |\n| [TCAD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftcad%3A) | 25, 24, 23, 22, 21                                           | -                                                            |\n| [TC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ftc%3A) | 25, 24, 23, 22, 21                                           | -                                                            |\n| [ICSE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Ficse%3A) | [25](https:\u002F\u002Fconf.researchr.org\u002Ftrack\u002Ficse-2025\u002Ficse-2025-research-track), [23](https:\u002F\u002Fconf.researchr.org\u002Ftrack\u002Ficse-2023\u002Ficse-2023-technical-track?#event-overview), 21 | -                                                            |\n| [FOCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Ffocs%3A) | -                                                            | -                                                            |\n| [STOC](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Fstoc%3A) | -                                                            | -                                                            |\n\n\u003C\u002Fdetails>\n\n\n\n\n**关键词**\n\n统计：:fire: 代码可获取且星标 >= 100 | :star: 引用次数 >= 50 | :mortar_board: 顶级期刊 \n\n**`kg.`**: 知识图谱 |   **`data.`**: 数据集  |   **`surv.`**: 调查\n\n\n\n\n\n\n## 联邦学习在顶级期刊中的发表情况\n\nNature（及其子刊）、Cell、Science（及其子刊）和 PNAS 中关于联邦学习的论文，可通过 [WOS](https:\u002F\u002Fwww.webofscience.com\u002Fwos\u002Fwoscc\u002Fsummary\u002Fed3f4552-5450-4de7-bf2c-55d01e20d5de-4301299e\u002Frelevance\u002F1) 检索引擎查询。\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级期刊中的发表情况\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-tier-journal -->\n\n|标题                                                           |    所属机构    |    发表期刊                    |    年份    |    资料|\n| ------------------------------------------------------------ | ----------- | --------------------- | ---- | ------------------------------------------------------------ |\n| 基于全同态加密的计算高效拜占庭鲁棒联邦学习 |  | Nat. Mach. Intell. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-01107-6)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2408.06197)] [[代码](https:\u002F\u002Fgithub.com\u002Fsiyang-jiang\u002FLancelot)] |\n| 激励模型共享市场中的包容性贡献 |  | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-62959-5)] [[代码](https:\u002F\u002Fgithub.com\u002F19dx\u002FiPFL)] |\n| FedECA：用于分布式环境中生存时间数据因果推断的联邦外部对照臂 |  | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-62525-z)] [[代码](https:\u002F\u002Fgithub.com\u002Fowkin\u002Ffedeca)] |\n| 使用FedProt进行隐私保护的多中心差异蛋白质丰度分析 |  | Nat. Comput. Sci. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-025-00832-7)] [[代码](https:\u002F\u002Fgithub.com\u002FFreddsle\u002FFedProt)] |\n| 通过联邦肿瘤分割挑战实现医疗AI算法的公平去中心化基准测试 |  | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-60466-1)] [[代码](https:\u002F\u002Fgithub.com\u002Fmlcommons\u002Fmedperf\u002Ftree\u002Ffets-challenge)] |\n| 一种应用于胸部X光的完全开放的AI基础模型 |  | Nature | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-025-09079-8)] [[代码](https:\u002F\u002Fgithub.com\u002Fjlianglab\u002FArk)] |\n| 利用具有原位物理不可克隆函数和真随机数发生器的忆阻器存内计算芯片进行联邦学习 |  | Nat. Electron. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41928-025-01390-6)] |\n| 基于联邦元学习重构个性化物联网的框架 | 中山大学 | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-59217-z)] [[代码](https:\u002F\u002Fgithub.com\u002FIntelligentSystemsLab\u002Fgeneric_and_open_learning_federator\u002F)] |\n| 在联邦医学影像中实现灵活的公平性度量 | 香港中文大学 | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-58549-0)] [[代码](https:\u002F\u002Fzenodo.org\u002Frecords\u002F15203267)] |\n| 向面向公平且隐私保护的医疗增强协作学习迈进 | 哈尔滨工业大学 | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-58055-3)] [[代码](https:\u002F\u002Fgithub.com\u002Fparidis-11\u002FDynamicFL)] |\n| 基于知识蒸馏的药物发现中的数据驱动联邦学习 | Lhasa Limited | Nat. Mach. Intell. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-00991-2)] [[代码](https:\u002F\u002Fgithub.com\u002FLhasaLimited\u002FFLuID_POC)] |\n| 用于公平联邦模型的分布式交叉学习——基于加州五家医院数据的隐私保护预测 | 耶鲁大学；UCSD | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-56510-9)] |\n| 用于同时保护私有数据和深度学习模型的物理不可克隆存内计算 | 北京大学 | Nat. Commun. | 2025 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-56412-w)] [[新闻](https:\u002F\u002Fic.pku.edu.cn\u002Fkxyj\u002Fkycg1\u002Fd2c084006150492c93ae3e6b0cb1d7df.htm)] |\n| MatSwarm：可信蜂群迁移学习驱动的材料计算，用于安全的大数据共享 | USTB；NTU | Nat. Commun. | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-53431-x)] [[代码](https:\u002F\u002Fgithub.com\u002FSICC-Group\u002FMatSwarm)] |\n| 通过联邦分割学习将边缘智能引入智能电表 | 香港大学 | Nat. Commun. | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-53352-9)] [[新闻](https:\u002F\u002Fwww.ces.org.cn\u002Fhtml\u002Freport\u002F24110829-1.htm)] |\n| 一项国际研究展示了一种用于儿童脑肿瘤的联邦学习AI平台 | 斯坦福大学 | Nat. Commun. | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-51172-5)] [[代码](https:\u002F\u002Fgithub.com\u002Fedhlee\u002FFLPedBrain)] |\n| PPML-Omics：一种保护患者隐私的联邦机器学习方法，适用于组学数据 | KAUST | Science Advances | 2024 | [[出版物](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fsciadv.adh8601)] [[代码](https:\u002F\u002Fgithub.com\u002FJoshuaChou2018\u002FPPML-Omics)] |\n| 神经网络伦理问题并非联邦学习所能解决 | TUM；UvA | Nat. Mach. Intell.(评论) | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-024-00813-x)] |\n| 用于识别胃癌术后高风险复发患者的鲁棒联邦学习模型 | 江门市中心医院；桂林航天工业学院；桂林电子科技大学； | Nat. Commun. | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-44946-4)] [[代码](https:\u002F\u002Fgithub.com\u002Fbaofengguat\u002FRFLM-project\u002F)] |\n| 无需优秀教师的隐私保护联邦蒸馏中的选择性知识共享 | 香港科技大学 | Nat. Commun. | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-44383-9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01731)] [[代码](https:\u002F\u002Fgithub.com\u002Fshaojiawei07\u002FSelective-FD)] |\n| 欧洲精准肿瘤学的联邦学习系统：DigiONE | IQVIA Cancer Research BV | Nat. Med. (评论) | 2024 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-023-02715-8)] |\n| 基于Qline架构的多客户端分布式盲量子计算 | 罗马萨皮恩扎大学 | Nat. Commun. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43617-0)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05195)] |\n| 设备无关的量子随机性增强零知识证明 | 中国科学技术大学 | PNAS | 2023 | [[出版物](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002F10.1073\u002Fpnas.2205463120)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.06717)] [[新闻](https:\u002F\u002Fwww.nsfc.gov.cn\u002Fpublish\u002Fportal0\u002Ftab448\u002Finfo90817.htm)] |\n| 通过联邦机器学习实现盈利性直接回收的协作式、隐私保护退役电池分类 | 清华大学 | Nat. Commun. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43883-y)] |\n| 倡导神经数据隐私与神经技术监管 | 哥伦比亚大学 | Nat. Protoc. (观点) | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41596-023-00873-0)] |\n| 通过MedPerf进行医疗人工智能的联邦基准测试 | IHU斯特拉斯堡；斯特拉斯堡大学；达纳-法伯癌症研究所；威尔康奈尔医学院；哈佛T.H.陈公共卫生学院；MIT；英特尔 | Nat. Mach. Intell. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-023-00652-2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.01406)] [[代码](https:\u002F\u002Fgithub.com\u002Fmlcommons\u002FMedPerf)] |\n| 医疗与健康领域的人工智能算法公平性 | 哈佛医学院；哈佛-麻省理工学院布罗德研究所；达纳-法伯癌症研究所 | Nat. Biomed. Eng. (观点) | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-023-01056-8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00603)] |\n| 用于联邦学习的差分隐私知识转移 | 清华大学 | Nat. Commun. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-38794-x)] [[代码](https:\u002F\u002Fgithub.com\u002Ftaoqi98\u002FPrivateKT)] |\n| 通过代理模型共享实现去中心化联邦学习 | Layer 6 AI；滑铁卢大学；Vector Institute | Nat. Commun. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-38569-4)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.11343)] [[代码](https:\u002F\u002Fgithub.com\u002Flayer6ai-labs\u002FProxyFL)] |\n| 符合数据保护法规的研究中的联邦机器学习 | 汉堡大学 | Nat. Mach. Intell.(评论) | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00601-5)] |\n| 用于预测三阴性乳腺癌新辅助化疗组织学反应的联邦学习 | Owkin | Nat. Med. | 2023 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-022-02155-3)] [[代码](https:\u002F\u002Fgithub.com\u002FSubstra\u002Fsubstra)] |\n| 稀有癌症边界检测中的联邦学习助力大数据应用 | 宾夕法尼亚大学 | Nat. Commun. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-33407-5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10836)] [[代码](https:\u002F\u002Fgithub.com\u002FFETS-AI\u002FFront-End)] |\n| 神经网络伦理问题并非联邦学习所能解决 | Hugging Face | Nat. Mach. Intell. (评论) | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00551-2)] |\n| 用于无监督脑部异常检测的联邦解耦表示学习 | TUM | Nat. Mach. Intell. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00515-2)] [[PDF](https:\u002F\u002Fdoi.org\u002Fhttps:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-722389\u002Fv1)] [[代码](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.6604161)] |\n| 将医疗领域的机器学习从开发转向部署，从模型转向数据 | 斯坦福大学；Greenstone Biosciences | Nat. Biomed. Eng. (综述文章) | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-022-00898-3)] |\n| 一种用于隐私保护个性化的联邦图神经网络框架 | 清华大学 | Nat. Commun. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30714-9)] [[代码](https:\u002F\u002Fgithub.com\u002Fwuch15\u002FFedPerGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F487383715)] |\n| 基于知识蒸馏的通信效率型联邦学习 | 清华大学 | Nat. Commun. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-29763-x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.13323)] [[代码](https:\u002F\u002Fzenodo.org\u002Frecord\u002F6383473)] |\n| 引领无线边缘人工智能的联邦神经形态学习 | 厦门大学；NTU | Nat. Commun. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-32020-3)] [[代码](https:\u002F\u002Fgithub.com\u002FGOGODD\u002FFL-EDGE-COMPUTING\u002Freleases\u002Ftag\u002Ffederated_learning)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F549087420)] |\n| 一种新颖的去中心化联邦学习方法，可用于在全球范围内分布、质量较差且受隐私保护的医疗数据上训练 | 伍伦贡大学 | Sci. Rep. | 2022 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-022-12833-3)] |\n| 通过人工智能中的隐私保护协作推进COVID-19诊断 | 华中科技大学 | Nat. Mach. Intell. | 2021 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00421-2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09461)] [[代码](https:\u002F\u002Fgithub.com\u002FHUST-EIC-AI-LAB\u002FUCADI)] |\n| 用于预测COVID-19患者临床结局的联邦学习 | MGH放射科和哈佛医学院 | Nat. Med. | 2021 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3)] [[代码](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3#code-availability)] |\n| 隐私保护协作式机器学习中的对抗干扰及其缓解措施 | 伦敦帝国理工学院；TUM；OpenMined | Nat. Mach. Intell.(观点) | 2021 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00390-3)] |\n| 分散式且保密的临床机器学习：蜂群学习 :star: | DZNE；波恩大学； | Nature :mortar_board: | 2021 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-021-03583-3)] [[代码](https:\u002F\u002Fgithub.com\u002FHewlettPackard\u002Fswarm-learning)] [[软件](https:\u002F\u002Fmyenterpriselicense.hpe.com)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F379434722)] |\n| 多机构医学影像上的端到端隐私保护深度学习 | TUM；伦敦帝国理工学院；OpenMined | Nat. Mach. Intell. | 2021 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-021-00337-8)] [[代码](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.4545599)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F484801505)] |\n| 通信效率型联邦学习 | 香港中文大学；普林斯顿大学 | PANS. | 2021 | [[出版物](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002Ffull\u002F10.1073\u002Fpnas.2024789118)] [[代码](https:\u002F\u002Fcode.ihub.org.cn\u002Fprojects\u002F4394\u002Frepository\u002Frevisions\u002Fmaster\u002Fshow\u002FPNAS)] |\n| 利用合成X光片打破医疗数据共享壁垒 | 亚琛工业大学 | Science. Advances. | 2020 | [[出版物](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fsciadv.abb7973)] [[代码](https:\u002F\u002Fgithub.com\u002Fpeterhan91\u002FThorax_GAN)] |\n| 医疗影像中的安全、隐私保护且联邦的机器学习 :star: | TUM；伦敦帝国理工学院；OpenMined | Nat. Mach. Intell.(观点) | 2020 | [[出版物](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-020-0186-1)] |\n\n\u003C!-- 结束：联邦学习在顶级人工智能会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n\n\n## 联邦学习在顶级人工智能会议和期刊中\n\n联邦学习相关论文被顶级人工智能（Artificial Intelligence）会议和期刊接收，包括 [IJCAI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fijcai\u002Findex.html)（国际人工智能联合会议）、[AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Faaai\u002Findex.html)（AAAI 人工智能会议）、[AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Faistats\u002Findex.html)（人工智能与统计学会议）、[ALT](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Falt\u002Findex.html)（国际算法学习理论会议）、[AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fai\u002Findex.html)（人工智能期刊）。\n\n- [IJCAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AIJCAI%3A) [2025](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F)、[2024](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F)、[2023](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F)、[2022](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F)、[2021](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F)、[2020](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F)、[2019](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F)\n- [AAAI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAAAI%3A) [2025](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2025.html)、[2024](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2024.html)、[2023](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Faaai\u002Faaai2023)、[2022](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-22\u002Fwp-content\u002Fuploads\u002F2021\u002F12\u002FAAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf)、[2021](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-21\u002Fwp-content\u002Fuploads\u002F2020\u002F12\u002FAAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf)、[2020](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FAAAI-20-Accepted-Paper-List.pdf)\n- [AISTATS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AAISTATS%3A) [2025](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002F)、[2024](http:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002F)、[2023](http:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002F)、[2022](http:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002F)、[2021](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002F)、[2020](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002F)\n- [ALT](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Aconf%2Falt%3A) 2022 年\n- [AI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fai%3A) 2025 年、2023 年\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级人工智能会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：联邦学习在顶级人工智能会议和期刊中 -->\n\n|Title                                                           |    Affiliation                                                     |    Venue      |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | ---- | ------------------------------------------------------------ |\n| Exploiting Label Skewness for Spiking Neural Networks in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F767)] |\n| FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F379)] |\n| Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F610)] |\n| Pixel-wise Divide and Conquer for Federated Vessel Segmentation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F540)] |\n| Universal Backdoor Defense via Label Consistency in Vertical Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F528)] |\n| Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F536)] |\n| Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F541)] [[COCE](https:\u002F\u002Fgithub.com\u002FlancasterJie\u002FFLAYER)] |\n| FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F559)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdingbinb\u002FFedDLAD)] |\n| Federated Multi-view Graph Clustering with Incomplete Attribute Imputation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F570)] |\n| ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F585)] |\n| Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F590)] |\n| FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F597)] |\n| FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F602)] |\n| FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F333)] |\n| FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F612)] |\n| Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F146)] |\n| Distilling A Universal Expert from Clustered Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F620)] |\n| CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F621)] |\n| FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F628)] |\n| Hypernetwork Aggregation for Decentralized Personalized Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F161)] |\n| Federated Domain Generalization with Decision Insight Matrix |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F633)] |\n| Generic Adversarial Attack Framework Against Vertical Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F646)] |\n| One-shot Federated Learning Methods: A Practical Guide |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1174)] |\n| Federated Learning at the Forefront of Fairness: A Multifaceted Perspective |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1177)] |\n| Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F670)] |\n| Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F677)] |\n| FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F692)] [[CODE](https:\u002F\u002Fgithub.com\u002FYuxia-Sun\u002FFL_FedAPA)] |\n| An Empirical Study of Federated Prompt Learning for Vision Language Model |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1188)] |\n| FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F706)] |\n| Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F733)] |\n| DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F744)] |\n| Backdoor Attack on Vertical Federated Graph Neural Network Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F877)] |\n| Federated Low-Rank Adaptation for Foundation Models: A Survey |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F1196)] |\n| Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F761)] |\n| FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F770)] |\n| A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F771)] |\n| Federated Stochastic Bilevel Optimization with Fully First-Order Gradients |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F784)] |\n| AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F787)] |\n| Rethinking Federated Graph Learning: A Data Condensation Perspective |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F775)] |\n| MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F886)] |\n| Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F798)] |\n| Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F808)] |\n| Inconsistency-Based Federated Active Learning |  | IJCAI | 2025 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2025\u002F812)] |\n| Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fthakur25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAnshThakur\u002FFedMode)] |\n| FedBaF: Federated Learning Aggregation Biased by a Foundation Model |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fpark25b.html)] |\n| Global Group Fairness in Federated Learning via Function Tracking |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Frychener25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyvesrychener\u002FFair-FL)] |\n| On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fzeng25b.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdunzeng\u002FFedAWARE)] |\n| Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Flabbi25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLabbi-Safwan\u002FFed-UCBVI)] |\n| ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fozkara25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkazkara\u002Fadept)] |\n| Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkhellaf25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FRemiKhellaf\u002FFedCausal-RCTs-Khellaf\u002F)] |\n| The cost of local and global fairness in Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fduan25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpapersubmission678\u002FThe-cost-of-local-and-global-fairness-in-FL)] |\n| Federated Communication-Efficient Multi-Objective Optimization |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Faskin25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Faskinb\u002FFedCMOO)] |\n| Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fmangold25a.html)] [[CODE](https:\u002F\u002Fpmangold.fr\u002Fpapers\u002Ffed-richardson-romberg\u002Fsupplementary.zip)] |\n| Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fzhang25l.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FBernie0115\u002FLR-BPFL)] |\n| On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkeshri25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FSatishKeshri\u002FContinual_FL)] |\n| DPFL: Decentralized Personalized Federated Learning |  | AISTATS | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv258\u002Fkharrat25a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsalmakh1\u002FDPFL)] |\n| FedHM: Efficient federated learning for heterogeneous models via low-rank factorization |  | AI | 2025 | [[PUB](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0004370225000529)] |\n| Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34475)] |\n| Single-Loop Federated Actor-Critic across Heterogeneous Environments |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34469)] |\n| Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34468)] |\n| FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34464)] |\n| FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34459)] |\n| ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34449)] |\n| Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34438)] |\n| Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33455)] |\n| Asynchronous Federated Clustering with Unknown Number of Clusters |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34429)] |\n| Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34415)] |\n| HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34409)] |\n| LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34404)] |\n| Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33440)] |\n| Modeling Inter-Intra Heterogeneity for Graph Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34378)] |\n| pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34368)] |\n| First-Order Federated Bilevel Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34355)] |\n| GAS: Generative Activation-Aided Asynchronous Split Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35503)] |\n| FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35497)] |\n| Federated Graph Condensation with Information Bottleneck Principles |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33417)] |\n| A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract) |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35318)] |\n| Federated Learning with Sample-level Client Drift Mitigation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35480)] |\n| Pilot: Building the Federated Multimodal Instruction Tuning Framework |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35476)] |\n| Flexible Sharpness-Aware Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35475)] |\n| MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32076)] |\n| PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35460)] |\n| Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35458)] |\n| Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35444)] |\n| Federated Recommendation with Explicitly Encoding Item Bias |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33395)] |\n| Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34733)] |\n| Decentralized Federated Learning with Model Caching on Mobile Agents |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35429)] |\n| Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35426)] |\n| FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35423)] |\n| Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35409)] |\n| Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35405)] |\n| Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35398)] |\n| Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32807)] |\n| Reputation-aware Revenue Allocation for Auction-based Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34296)] |\n| Learn How to Query from Unlabeled Data Streams in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34287)] |\n| Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract) |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35304)] |\n| Graph Consistency and Diversity Measurement for Federated Multi-View Clustering |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34277)] |\n| WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34272)] |\n| Label-Free Backdoor Attacks in Vertical Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34246)] |\n| Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35054)] |\n| FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34228)] |\n| Fair Federated Survival Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34214)] |\n| Federated t-SNE and UMAP for Distributed Data Visualization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34204)] |\n| Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34201)] |\n| Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34197)] |\n| In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34192)] |\n| Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34187)] |\n| Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33572)] |\n| Federated Unlearning with Gradient Descent and Conflict Mitigation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34181)] |\n| Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32671)] |\n| FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34172)] |\n| FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34129)] |\n| Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34126)] |\n| FedCross: Intertemporal Federated Learning Under Evolutionary Games |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34104)] |\n| Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34094)] |\n| SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34090)] |\n| Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33328)] |\n| Federated Graph-Level Clustering Network |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34077)] |\n| LiD-FL: Towards List-Decodable Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34072)] |\n| Convergence Analysis of Federated Learning Methods Using Backward Error Analysis |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34060)] |\n| Progressive Distribution Matching for Federated Semi-Supervised Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32551)] |\n| TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34053)] |\n| Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34047)] |\n| EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34046)] |\n| FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F34007)] |\n| pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33980)] |\n| FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33975)] |\n| FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33942)] |\n| Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35332)] |\n| PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33912)] |\n| TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33524)] |\n| FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33878)] |\n| DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33877)] |\n| Federated Assemblies |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33520)] |\n| Federated Causally Invariant Feature Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33866)] |\n| A New Federated Learning Framework Against Gradient Inversion Attacks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33865)] |\n| Exploring Vacant Classes in Label-Skewed Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33864)] |\n| Capture Global Feature Statistics for One-Shot Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33862)] |\n| Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33839)] |\n| MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F32313)] |\n| Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33830)] |\n| Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33829)] |\n| Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33822)] |\n| SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33820)] |\n| Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33814)] |\n| How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33788)] |\n| Attribute Inference Attacks for Federated Regression Tasks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33787)] |\n| Federated Binary Matrix Factorization Using Proximal Optimization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33773)] |\n| Creating Coherence in Federated Non-Negative Matrix Factorization |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33772)] |\n| Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33764)] |\n| DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33746)] |\n| Federated Foundation Models on Heterogeneous Time Series |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33739)] |\n| FedPop: Federated Population-based Hyperparameter Tuning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33732)] |\n| Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F35005)] |\n| EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33700)] |\n| Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning |  | AAAI | 2025 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F33678)] |\n| Federated Multi-View Clustering via Tensor Factorization |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F438)] |\n| Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F439)] |\n| LG-FGAD: An Effective Federated Graph Anomaly Detection Framework |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F416)] |\n| Federated Prompt Learning for Weather Foundation Models on Devices |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F638)] |\n| Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F419)] |\n| Unlearning during Learning: An Efficient Federated Machine Unlearning Method |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F446)] |\n| Practical Hybrid Gradient Compression for Federated Learning Systems |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F458)] |\n| Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F450)] [[CODE](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedACD)] |\n| Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F457)] [[CODE](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedACD)] |\n| Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F788)] |\n| FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F501)] |\n| DarkFed: A Data-Free Backdoor Attack in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F491)] |\n| Scalable Federated Unlearning via Isolated and Coded Sharding |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F503)] |\n| Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F238)] |\n| Label Leakage in Vertical Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F902)] |\n| The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F980)] |\n| LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F515)] |\n| EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F51)] |\n| Knowledge Distillation in Federated Learning: A Practical Guide |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F905)] |\n| FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F526)] |\n| FedPFT: Federated Proxy Fine-Tuning of Foundation Models |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F531)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpzp-dzd\u002FFedPFT)] |\n| A Systematic Survey on Federated Semi-supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F911)] |\n| Intelligent Agents for Auction-based Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F912)] |\n| A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F552)] |\n| Dual Calibration-based Personalised Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F551)] |\n| Stakeholder-oriented Decision Support for Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F972)] |\n| Redefining Contributions: Shapley-Driven Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F554)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftnurbek\u002Fshapfed}{https:\u002F\u002Fgithub.com\u002Ftnurbek\u002Fshapfed)] |\n| A Survey on Efficient Federated Learning Methods for Foundation Model Training |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F919)] |\n| From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F575)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002FFFL4MIA)] |\n| FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F585)] |\n| FedFa: A Fully Asynchronous Training Paradigm for Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F584)] |\n| FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F594)] |\n| FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F599)] |\n| Personalized Federated Learning for Cross-City Traffic Prediction |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F611)] |\n| Federated Adaptation for Foundation Model-based Recommendations |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F603)] |\n| BADFSS: Backdoor Attacks on Federated Self-Supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F61)] |\n| Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F290)] [[CODE](https:\u002F\u002Fgithub.com\u002FGuogangZhu\u002FFedDB)] |\n| FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning |  | IJCAI | 2024 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F632)] |\n| BOBA: Byzantine-Robust Federated Learning with Label Skewness | UIUC | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fbao24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12932)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002FBOBA)] |\n| Federated Linear Contextual Bandits with Heterogeneous Clients | University of Virginia | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fblaser24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.00116)] [[CODE](https:\u002F\u002Fgithub.com\u002Fblaserethan\u002FHetoFedBandit)] |\n| Federated Experiment Design under Distributed Differential Privacy | Stanford University; Meta | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fchen24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.04375)] [[CODE](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g\u002Fview?usp=drive_link)] |\n| Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression | Princeton University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fchen24d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19059)] |\n| Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Feven24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00465)] |\n| SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Ffraboni24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.11656)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning\u002Ftree\u002FSIFU)] |\n| Compression with Exact Error Distribution for Federated Learning | École Polytechnique | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fhegazy24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.20682)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmahegz\u002FCompWithExactError)] |\n| Adaptive Federated Minimax Optimization with Lower Complexities | NJU; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fhuang24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07303)] |\n| Adaptive Compression in Federated Learning via Side Information | Stanford University; University of Padova | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fisik24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.12625)] [[CODE](https:\u002F\u002Fgithub.com\u002FFrancescoPase\u002FFederated-KLMS)] |\n| On-Demand Federated Learning for Arbitrary Target Class Distributions | UNIST | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fjeong24a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Feai-lab\u002FOn-DemandFL)] |\n| FedFisher: Leveraging Fisher Information for One-Shot Federated Learning | CMU | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fjhunjhunwala24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.12329)] [[CODE](https:\u002F\u002Fgithub.com\u002FDivyansh03\u002FFedFisher)] |\n| Queuing dynamics of asynchronous Federated Learning | Huawei | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fleconte24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.00017)] |\n| Personalized Federated X-armed Bandit | Purdue University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fli24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.16323)] [[CODE](https:\u002F\u002Fgithub.com\u002FWilliamLwj\u002FPyXAB)] |\n| Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks | University of Oxford | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fmolaei24a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAnshThakur\u002FFL4HeterogenousEHRs)] |\n| Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization | University of Virginia | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fshen24c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00944)] |\n| Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters | Northwestern University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fsun24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03824)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffedcodexx\u002FGeneralization-of-Federated-Learning)] |\n| Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains | Sofia University | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Ftsoy24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.06672)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnikita-tsoy98\u002Fmutually-beneficial-federated-learning-replication)] |\n| Analysis of Privacy Leakage in Federated Large Language Models | University of Florida | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fvu24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.04784)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvunhatminh\u002FFL_Attacks.git)] |\n| Invariant Aggregator for Defending against Federated Backdoor Attacks | UIUC | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fwang24e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01834)] [[CODE](https:\u002F\u002Fgithub.com\u002FXiaoyang-Wang\u002FInvariantAggregator)] |\n| Communication-Efficient Federated Learning With Data and Client Heterogeneity | ISTA | AISTATS | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv238\u002Fzakerinia24a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10032)] [[CODE](https:\u002F\u002Fgithub.com\u002FShayanTalaei\u002FQuAFL)] |\n| FedMut: Generalized Federated Learning via Stochastic Mutation | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29146)] |\n| Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization | Carleton University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29562)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93915-federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] |\n| No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation | IIT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28950)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93775-no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10080)] [[CODE](https:\u002F\u002Fgithub.com\u002Fanujksirohi\u002FF2PGNN-AAAI24)] |\n| Formal Logic Enabled Personalized Federated Learning through Property Inference | Vanderbilt University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28962)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.07448)] |\n| Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks | Illinois Tech | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28965)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91722-task-agnostic-privacy-preserving-representation-learning-for-federated-learning-against-attribute-inference-attacks)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06989)] [[CODE](https:\u002F\u002Fgithub.com\u002FTAPPFL\u002FTAPPFL)] |\n| FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning | Leibniz University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28971)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93537-fairtrade-achieving-pareto-optimal-trade-offs-between-balanced-accuracy-and-fairness-in-federated-learning)] |\n| Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators | HKUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28974)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92397-combating-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05358)] |\n| Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity | UT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29025)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93417-fed-qssl-a-framework-for-personalized-federated-learning-under-bitwidth-and-data-heterogeneity)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13380)] |\n| On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning | University of Virginia | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29010)] |\n| FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning | LMU Munich Siemens AG | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29007)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91710-feddat-an-approach-for-foundation-model-finetuning-in-multi-modal-heterogeneous-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12305)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaokunChen245\u002FFedDAT)] |\n| Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models | Xi'an Jiaotong University Shaanxi Joint Key Laboratory for Artificial Intelligence | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29012)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91776-watch-your-head-assembling-projection-heads-to-save-the-reliability-of-federated-models)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.16255)] |\n| FedGCR: Achieving Performance and Fairness for  Federated Learning with Distinct Client Types via Group Customization  and Reweighting | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29031)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92275-fedgcr-achieving-performance-and-fairness-for-federated-learning-with-distinct-client-types-via-group-customization-and-reweighting)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcelinezheng\u002Ffedgcr)] |\n| Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation | Xiamen University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27909)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91824-federated-modality-specific-encoders-and-multimodal-anchors-for-personalized-brain-tumor-segmentation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.11803)] [[CODE](https:\u002F\u002Fgithub.com\u002Fqdaiing\u002Ffedmema)] |\n| Exploiting Label Skews in Federated Learning with Model Concatenation | NUS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29063)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92569-exploiting-label-skews-in-federated-learning-with-model-concatenation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06290)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsjtudyq\u002FFedConcat)] |\n| Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning | SUST; HKUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29958)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92937-complementary-knowledge-distillation-for-robust-and-privacy-preserving-model-serving-in-vertical-federated-learning)] |\n| Federated Learning via Input-Output Collaborative Distillation | University at Buffalo; USA Harvard Medical School | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30209)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94089-federated-learning-via-input-output-collaborative-distillation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.14478)] [[CODE](https:\u002F\u002Fgithub.com\u002Flsl001006\u002Ffediod)] |\n| Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space | University of Waterloo Vector Institute | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29122)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92727-calibrated-one-round-federated-learning-with-bayesian-inference-in-the-predictive-space)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.09817)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhasanmohsin\u002FbetaPredBayesFL)] |\n| FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning | HFUT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29113)] [[PDF](https:\u002F\u002Fgithub.com\u002FXianjie-Guo\u002FFedCSL)] |\n| FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning | Xi'an Jiaotong University;  Leiden University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29179)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92327-fedfixer-mitigating-heterogeneous-label-noise-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.16561)] |\n| FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing | NJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29181)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93122-fedlps-heterogeneous-federated-learning-for-multiple-tasks-with-local-parameter-sharing)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.08578)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjyzgh\u002FFedLPS)] |\n| Provably Convergent Federated Trilevel Learning | TJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29190)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.11835)] |\n| Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts | U-M | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29191)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93963-performative-federated-learning-a-solution-to-model-dependent-and-heterogeneous-distribution-shifts)] |\n| General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized  Services of Multi-Merchants | Kyung Hee University;  Harex InfoTech | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30309)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91475-general-commerce-intelligence-glocally-federated-nlp-based-engine-for-privacy-preserving-and-sustainable-personalized-services-of-multi-merchants)] |\n| EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning | Sony AI | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29258)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91709-emgan-early-mix-gan-on-extracting-server-side-model-in-split-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlijingtao\u002FSFL-MEA)] |\n| FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels | SYSU; HKU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28095)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91764-feddiv-collaborative-noise-filtering-for-federated-learning-with-noisy-labels)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12263)] [[CODE](https:\u002F\u002Fgithub.com\u002Flijichang\u002FFLNL-FedDiv)] |\n| Point Transformer with Federated Learning for  Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained  Whole Slide Images | USTC; CAS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28082)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92706-point-transformer-with-federated-learning-for-predicting-breast-cancer-her2-status-from-hematoxylin-and-eosin-stained-whole-slide-images)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.06454)] [[CODE](https:\u002F\u002Fgithub.com\u002FBoyden\u002FPointTransformerFL)] |\n| FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning | CAS | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29254)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.02734)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsuperlj666\u002Ffedns)] |\n| Federated X-armed Bandit | Purdue University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29267)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93049-federated-x-armed-bandit)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15268)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwilliamlwj\u002Fpyxab)] |\n| Algorithmic Foundation of Federated Learning with Sequential Data | GMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30291)] |\n| UFDA: Universal Federated Domain Adaptation with Practical Assumptions | XJTU; University of Sydney | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29311)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93578-ufda-universal-federated-domain-adaptation-with-practical-assumptions)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.15570)] [[CODE](https:\u002F\u002Fgithub.com\u002FXinhui-99\u002FUFDA)] |\n| FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update | Hithink RoyalFlush Information Network Co | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29297)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92855-fedasmu-efficient-asynchronous-federated-learning-with-dynamic-staleness-aware-model-update)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05770)] |\n| Language-Guided Transformer for Federated Multi-Label Classification | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29295)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93447-language-guided-transformer-for-federated-multi-label-classification)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.07165)] [[CODE](https:\u002F\u002Fgithub.com\u002FJack24658735\u002FFedLGT)] |\n| FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers | SZU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28175)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92166-fedcd-federated-semi-supervised-learning-with-class-awareness-balance-via-dual-teachers)] [[CODE](https:\u002F\u002Fgithub.com\u002FYunzZ-Liu\u002FFedCD\u002F)] |\n| Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning | HEU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30131)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94230-beyond-traditional-threats-a-persistent-backdoor-attack-on-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002FPhD-TaoLiu\u002FFCBA)] |\n| Federated Learning with Extremely Noisy Clients via Negative Distillation | XMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29329)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93309-federated-learning-with-extremely-noisy-clients-via-negative-distillation)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12703)] [[CODE](https:\u002F\u002Fgithub.com\u002FlinChen99\u002FFedNed)] |\n| FedST: Federated Style Transfer Learning for Non-IID Image Segmentation | USTB | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28199)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93609-fedst-federated-style-transfer-learning-for-non-iid-image-segmentation)] [[学报](https:\u002F\u002Fjournal.bupt.edu.cn\u002FCN\u002Fabstract\u002Fabstract5178.shtml)] [[CODE](https:\u002F\u002Fgithub.com\u002FYoferChen\u002FFedST)] |\n| PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning | USTC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29339)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92243-ppidsg-a-privacy-preserving-image-distribution-sharing-scheme-with-gan-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10380)] [[CODE](https:\u002F\u002Fgithub.com\u002Fytingma\u002FPPIDSG)] |\n| A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities | DCU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30400)] |\n| A Primal-Dual Algorithm for Hybrid Federated Learning | Northwestern University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29363)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93144-a-primal-dual-algorithm-for-hybrid-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08106)] |\n| FedLF: Layer-Wise Fair Federated Learning | CUHK; Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29368)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93087-fedlf-layer-wise-fair-federated-learning)] |\n| Towards Fair Graph Federated Learning via Incentive Mechanisms | ZJU; FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29365)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92583-towards-fair-graph-federated-learning-via-incentive-mechanisms)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13306)] [[CODE](https:\u002F\u002Fgithub.com\u002FChenglu0426\u002FFairGraphFL)] |\n| Towards the Robustness of Differentially Private Federated Learning | THU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29967)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92491-towards-the-robustness-of-differentially-private-federated-learning)] |\n| Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective | ZJU; HUAWEI | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29385)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94020-resisting-backdoor-attacks-in-federated-learning-via-bidirectional-elections-and-individual-perspective)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.16456)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenqincn\u002FSnowball)] |\n| Integer Is Enough: When Vertical Federated Learning Meets Rounding | ZJU; Ant Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29388)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93362-integer-is-enough-when-vertical-federated-learning-meets-rounding)] |\n| CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data | XMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29416)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92441-clip-guided-federated-learning-on-heterogeneity-and-long-tailed-data)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.08648)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshijiangming1\u002FCLIP2FL)] |\n| Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning | FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29434)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92772-federated-adaptive-prompt-tuning-for-multi-domain-collaborative-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07864)] [[CODE](https:\u002F\u002Fgithub.com\u002Fleondada\u002Ffedapt)] |\n| Multi-Dimensional Fair Federated Learning | SDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29430)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92619-multi-dimensional-fair-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05551)] |\n| HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation | ENN Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30317)] |\n| On the Role of Server Momentum in Federated Learning | University of Virginia | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29439)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12670)] |\n| FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants | BUPT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29446)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93158-fedcompetitors-harmonious-collaboration-in-federated-learning-with-competing-participants)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.11391)] |\n| z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning | CUHK; China Shenzhen Research Institute of Big Data | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29454)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93975-z-signfedavg-a-unified-stochastic-sign-based-compression-for-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.02589)] |\n| Data Disparity and Temporal Unavailability Aware  Asynchronous Federated Learning for Predictive Maintenance on  Transportation Fleets | Volkswagen Group | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29467)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92405-data-disparity-and-temporal-unavailability-aware-asynchronous-federated-learning-for-predictive-maintenance-on-transportation-fleets)] |\n| Federated Graph Learning under Domain Shift with Generalizable Prototypes | WHU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29468)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92526-federated-graph-learning-under-domain-shift-with-generalizable-prototypes)] |\n| TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients | Technical University of Munich | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29481)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91900-turbosvm-fl-boosting-federated-learning-through-svm-aggregation-for-lazy-clients)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.12012)] [[CODE](https:\u002F\u002Fgithub.com\u002FKasneci-Lab\u002FTurboSVM-FL)] |\n| Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization | TJU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29510)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.10272)] [[CODE](https:\u002F\u002Fgithub.com\u002Fweiyikang\u002FFedGM)] |\n| Concealing Sensitive Samples against Gradient Leakage in Federated Learning | Monash University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30171)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94147-concealing-sensitive-samples-against-gradient-leakage-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05724)] [[CODE](https:\u002F\u002Fgithub.com\u002FJingWu321\u002FDCS-2)] |\n| FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise | HUST | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29525)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92926-feda3i-annotation-quality-aware-aggregation-for-federated-medical-image-segmentation-against-heterogeneous-annotation-noise)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.12838)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002FFedAAAI)] |\n| Federated Causality Learning with Explainable Adaptive Optimization | SDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29566)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93217-federated-causality-learning-with-explainable-adaptive-optimization)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05540)] |\n| Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users | USTC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30045)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F93664-federated-contextual-cascading-bandits-with-asynchronous-communication-and-heterogeneous-users)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.16312)] |\n| Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models | FDU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29568)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.04063)] |\n| Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification | XMU; University of Trento | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28468)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91850-diversity-authenticity-co-constrained-stylization-for-federated-domain-generalization-in-person-re-identification)] |\n| PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search | U of T | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29576)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92749-perfedrlnas-one-for-all-personalized-federated-neural-architecture-search)] |\n| Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction | BUPT | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29603)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92183-efficient-asynchronous-federated-learning-with-prospective-momentum-aggregation-and-fine-grained-correction)] |\n| Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms | HKU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27796)] |\n| FedTGP: Trainable Global Prototypes with  Adaptive-Margin-Enhanced Contrastive Learning for Data and Model  Heterogeneity in Federated Learning | SJTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29617)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91976-fedtgp-trainable-global-prototypes-with-adaptive-margin-enhanced-contrastive-learning-for-data-and-model-heterogeneity-in-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.03230)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedTGP)] |\n| LR-XFL: Logical Reasoning-Based Explainable Federated Learning | NTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30179)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12681)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyanci87\u002Flr-xfl)] |\n| A Huber Loss Minimization Approach to Byzantine Robust Federated Learning | Zhejiang Lab | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30181)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F94170-a-huber-loss-minimization-approach-to-byzantine-robust-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12581)] |\n| Knowledge-Aware Parameter Coaching for Personalized Federated Learning | Northeastern University | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29651)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92711-knowledge-aware-parameter-coaching-for-personalized-federated-learning)] |\n| Federated Label-Noise Learning with Local Diversity Product Regularization | SJTU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29659)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F92697-federated-label-noise-learning-with-local-diversity-product-regularization)] [[SUPP](https:\u002F\u002Fwanglab.sjtu.edu.cn\u002Fuserfiles\u002Ffiles\u002FSupp_FedLNL.pdf)] |\n| Adapted Weighted Aggregation in Federated Learning (Student Abstract) | UBC | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30557)] |\n| Knowledge Transfer via Compact Model in Federated Learning (Student Abstract) | University of Sydney | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30498)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91519-knowledge-transfer-via-compact-model-in-federated-learning-student-abstract)] |\n| PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract) | The Ohio State University Auton Lab, CMU | AAAI | 2024 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F30438)] [[PAGE](https:\u002F\u002Funderline.io\u002Flecture\u002F91585-picsr-prototype-informed-cross-silo-router-for-federated-learning-student-abstract)] |\n| Privacy-preserving graph convolution network for federated item recommendation | SZU | AI | 2023 | [[PUB](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS000437022300142X)] |\n| Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation | CAS; UCAS; JD Technology; JD Intelligent Cities Research | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25531)] |\n| Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense | USTC; State Key Laboratory of Cognitive Intelligence | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25611)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.05399)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyflyl613\u002Ffedrec)] |\n| Incentive-Boosted Federated Crowdsourcing | SDU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25744)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14439)] |\n| Tackling Data Heterogeneity in Federated Learning with Class Prototypes | Lehigh University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25891)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02758)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyutong-dai\u002Ffednh)] |\n| FairFed: Enabling Group Fairness in Federated Learning | USC | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25911)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00857)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F613201113)] |\n| Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning | MSU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25955)] |\n| Complement Sparsification: Low-Overhead Model Pruning for Federated Learning | NJIT | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25977)] |\n| Almost Cost-Free Communication in Federated Best Arm Identification | NUS | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26010)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.09215)] |\n| Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning | University of Southern California Inha University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26023)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10302)] |\n| Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning | BJTU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26083)] |\n| FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26122)] |\n| Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning | USC | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26177)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.03328)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38960185\u002Fsecuring-secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nVV6S2sb_UL&name=supplementary_material)] |\n| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26187)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13009)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedstar)] |\n| Efficient  Distribution Similarity Identification in Clustered Federated Learning  via Principal Angles between Client Data Subspaces | UCSD | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26197)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.10526)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmmorafah\u002Fpacfl)] |\n| FedABC: Targeting Fair Competition in Personalized Federated Learning | WHU; Hubei Luojia Laboratory; JD Explore Academy | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26203)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.07450)] |\n| Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework | SUTD | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26212)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01519)] |\n| FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability | XMU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26223)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwwzzz\u002Ffedgs)] |\n| Faster Adaptive Federated Learning | University of Pittsburgh | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26235)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.00974)] |\n| FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation | HKUST | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26237)] [[CODE](https:\u002F\u002Fgithub.com\u002FCodePothunter\u002Ffednp)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3XM_NNvXCBo)] [[SUPP](https:\u002F\u002Fgithub.com\u002FCodePothunter\u002Ffednp\u002Fblob\u002Fmain\u002Fappendix.pdf)] |\n| Bayesian Federated Neural Matching That Completes Full Information | TJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26245)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.08010)] |\n| CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems | ZJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26246)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14216)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxjiajiahao\u002Ffederated-minimax)] |\n| Federated Generative Model on Multi-Source Heterogeneous Data in IoT | GSU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26252)] |\n| DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness | SUNY-Binghamton University | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26271)] |\n| FedALA: Adaptive Local Aggregation for Personalized Federated Learning | SJTU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26330)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01197)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedala)] |\n| Delving into the Adversarial Robustness of Federated Learning | ZJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26331)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09479)] |\n| On the Vulnerability of Backdoor Defenses for Federated Learning | TJU | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26393)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08170)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjinghuichen\u002Ffocused-flip-federated-backdoor-attack)] |\n| Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model | RUC; Engineering Research Center of Ministry of Education on Database and BI | AAAI | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26400)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.05516)] |\n| DPAUC: Differentially Private AUC Computation in Federated Learning | ByteDance Inc. | AAAI Special Tracks | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26770)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12294)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Ffedlearner)] |\n| Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout | NTU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26836)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.11485)] |\n| Industry-Scale Orchestrated Federated Learning for Drug Discovery | KU Leuven | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26847)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08871)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=J_RmZhKzBcA)] |\n| A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract) | CNU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26984)] |\n| MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract) | PolyU | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F26995)] |\n| Clustered Federated Learning for Heterogeneous Data (Student Abstract) | RUC | AAAI Special Programs | 2023 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27049)] |\n| FedSampling: A Better Sampling Strategy for Federated Learning | THU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F462)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.14245)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftaoqi98\u002FFedSampling)] |\n| HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F440)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.14384)] |\n| FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning | NTU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F394)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.05174)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcyyever\u002Fdistributed_learning_simulator)] |\n| Federated Probabilistic Preference Distribution Modelling with  Compactness Co-Clustering for Privacy-Preserving Multi-Domain  Recommendation | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F245)] |\n| Federated Graph Semantic and Structural Learning | WHU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F426)] |\n| BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning | SYSU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F498)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05221)] |\n| FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment | SYSU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F444)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06124)] |\n| FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation | Webank | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F418)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12623)] |\n| Globally Consistent Federated Graph Autoencoder for Non-IID Graphs | FZU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F419)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgcfgae\u002FGCFGAE)] |\n| Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning | NTU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F474)] |\n| Dual Personalization on Federated Recommendation | JLU; University of Technology Sydney | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F507)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08143)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhangcx19\u002Fijcai-23-pfedrec)] |\n| FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity | HUST | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F492)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05230)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnn2000\u002Ffednoro)] |\n| Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning | Xiangtan University | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F508)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10783)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhanghangtao\u002Fpoisoning-attack-on-fl)] |\n| FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks | CUHK | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F412)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.09729)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcynricfu\u002Ffedhgn)] |\n| FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer | Ping An Technology; THU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F443)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.15347)] |\n| Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data | UTS | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F393)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09152)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshengchaochen82\u002Fmetepfl)] |\n| FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training | ZJU | IJCAI | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F483)] [[CODE](https:\u002F\u002Fgithub.com\u002FHanzhouu\u002FFedBFPT)] |\n| Bayesian Federated Learning: A Survey |  | IJCAI Survey Track | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13267)] |\n| A Survey of Federated Evaluation in Federated Learning | Macquarie University | IJCAI Survey Track | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F758)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.08070)] |\n| SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract) | INSA Centre Val de Loire | IJCAI Journal Track | 2023 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2023\u002F772)] |\n| The communication cost of security and privacy in federated frequency estimation | Stanford | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fchen23e.html)] [[CODE](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1A3sp42a4RKswxjCOBAXlfUxBzL5IF431?usp=share_link)] |\n| Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout | Rice University | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fdun23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdunchen\u002FAsyncDrop__Release)] |\n| Federated Learning under Distributed Concept Drift | CMU | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fjothimurugesan23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FFedDrift)] |\n| Characterizing Internal Evasion Attacks in Federated Learning | CMU | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fkim23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftj-kim\u002FpFedDef_v1)] |\n| Federated Asymptotics: a model to compare federated learning algorithms | Stanford | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fcheng23b.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryxcheng\u002Fpersonalized-federated-learning)] |\n| Private Non-Convex Federated Learning Without a Trusted Server | USC | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Flowy23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fghafeleb\u002FPrivate-NonConvex-Federated-Learning-Without-a-Trusted-Server)] |\n| Federated Learning for Data Streams | Universit ́ e Cˆ ote d’Azur | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fmarfoq23a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fstreaming-fl)] |\n| Nothing but Regrets — Privacy-Preserving Federated Causal Discovery | Helmholtz Centre for Information Security | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fmian23a.html)] [[CODE](https:\u002F\u002Feda.rg.cispa.io\u002Fprj\u002Fperi\u002F)] |\n| Active Membership Inference Attack under Local Differential Privacy in Federated Learning | UFL | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fnguyen23e.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftrucndt\u002Fami)] |\n| Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms | CMAP | AISTATS | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Fplassier23a.html)] |\n| Byzantine-Robust Federated Learning with Optimal Statistical Rates | UC Berkeley | AISTATS | 2023 | [[PUB](https:\u002F\u002Fgithub.com\u002Fwanglun1996\u002Fsecure-robust-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwanglun1996\u002Fsecure-robust-federated-learning)] |\n| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13009)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedstar)] |\n| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwwzzz\u002Ffedgs)] |\n| Incentive-boosted Federated Crowdsourcing | SDU | AAAI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14439)] |\n| Towards Understanding Biased Client Selection in Federated Learning. | CMU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjee-cho22a.html)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjee-cho22a\u002Fjee-cho22a-supp.zip)] |\n| FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning | KAUST | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fgasanov22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.11556)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fgasanov22a\u002Fgasanov22a-supp.zip)] |\n| Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. | Stanford | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fglasgow22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.03741)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002Fsharp-bounds-for-fedavg-and-continuous-perspective)] |\n| Federated Reinforcement Learning with Environment Heterogeneity. | PKU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fjin22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.02634)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpengyang7881187\u002Ffedrl)] |\n| Federated Myopic Community Detection with One-shot Communication | Purdue | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fke22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07255)] |\n| Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. | University of Virginia | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fli22e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.01463)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcyrilli\u002FAsync-LinUCB)] |\n| Towards Federated Bayesian Network Structure Learning with Continuous Optimization. | CMU | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fng22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.09356)] [[CODE](https:\u002F\u002Fgithub.com\u002Fignavierng\u002Fnotears-admm)] |\n| Federated Learning with Buffered Asynchronous Aggregation | Meta AI | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fnguyen22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06639)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ui-OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar)] |\n| Differentially Private Federated Learning on Heterogeneous Data. | Stanford | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fnoble22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09278)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmaxencenoble\u002FDifferential-Privacy-for-Heterogeneous-Federated-Learning)] |\n| SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification | Princeton | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fpanda22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.06274)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsparsefed\u002Fsparsefed)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TXG7ZScheas&ab_channel=GoogleTechTalks)] |\n| Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning | KAUST | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fqian22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.01847)] |\n| Federated Functional Gradient Boosting. | University of Pennsylvania | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fshen22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06972)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FFederated-Learning-Pytorch)] |\n| QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. | Criteo AI Lab | AISTATS | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fvono22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00797)] [[CODE](https:\u002F\u002Fproceedings.mlr.press\u002Fv151\u002Fvono22a\u002Fvono22a-supp.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar)] |\n| Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting **`kg.`** | ZJU | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F273)] [[PDF](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2205.04692)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzjukg\u002Fmaker)] |\n| Personalized Federated Learning With a Graph | UTS | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F357)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.00829)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdawenzi098\u002FSFL-Structural-Federated-Learning)] |\n| Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification | ZJU | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F272)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11903)] |\n| Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F301)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.08394)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljaiverson\u002FpFL-APPLE)] |\n| Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F399)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.12703)] |\n| Private Semi-Supervised Federated Learning. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F279)] |\n| Continual Federated Learning Based on Knowledge Distillation. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.24963\u002Fijcai.2022\u002F306)] |\n| Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F308)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13399)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshangxinyi\u002FCReFF-FL)] |\n| Federated Multi-Task Attention for Cross-Individual Human Activity Recognition |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F475)] |\n| Personalized Federated Learning with Contextualized Generalization. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F311)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13044)] |\n| Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F106)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13256)] |\n| FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F324)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.08211)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederatedAI\u002Fresearch\u002Ftree\u002Fmain\u002Fpublications\u002FFedCG)] |\n| FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F385)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.11536)] |\n| Towards Verifiable Federated Learning **`surv.`** |  | IJCAI | 2022 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F792)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08310)] |\n| HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images | CUHK; BUAA | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F19993)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.10775)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmed-air\u002FHarmoFL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F472555067)] |\n| Federated Learning for Face Recognition with Gradient Correction | BUPT | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20095)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.07246)] |\n| SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data | USC | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20643)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02743)] [[CODE](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FSpreadGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F429720860)] |\n| SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures | HIT; PCL | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20345)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwudonglei99\u002Fsmartidx)] |\n| Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network | TJU | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F19878)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.08831)] |\n| Seizing Critical Learning Periods in Federated Learning | SUNY-Binghamton University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20859)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05613)] |\n| Coordinating Momenta for Cross-silo Federated Learning | University of Pittsburgh | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20853)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03970)] |\n| FedProto: Federated Prototype Learning over Heterogeneous Devices | UTS | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20819)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.00243)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuetan031\u002Ffedproto)] |\n| FedSoft: Soft Clustered Federated Learning with Proximal Local Updating | CMU | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20785)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.06053)] [[CODE](https:\u002F\u002Fgithub.com\u002Fycruan\u002FFedSoft)] |\n| Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better | The University of Texas at Austin | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20555)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.09824)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbibikar\u002Ffeddst)] |\n| FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition | National Taiwan University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20057)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12496)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjackie840129\u002Ffedfr)] |\n| SplitFed: When Federated Learning Meets Split Learning | CSIRO | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20825)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12088)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchandra2thapa\u002FSplitFed-When-Federated-Learning-Meets-Split-Learning)] |\n| Efficient Device Scheduling with Multi-Job Federated Learning | Soochow University | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F21235)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.05928)] |\n| Implicit Gradient Alignment in Distributed and Federated Learning | IIT Kanpur | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20597)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13897)] |\n| Federated Nearest Neighbor Classification with a Colony of Fruit-Flies | IBM Research | AAAI | 2022 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F20775)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.07157)] [[CODE](https:\u002F\u002Fgithub.com\u002Frithram\u002Fflynn)] |\n| Iterated Vector Fields and Conservatism, with Applications to Federated Learning. | Google | ALT | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv167\u002Fcharles22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.03973)] |\n| Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F202)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.01558)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35198)] |\n| Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F352)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12300)] |\n| FedSpeech: Federated Text-to-Speech with Continual Learning |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F527)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.07216)] |\n| Practical One-Shot Federated Learning for Cross-Silo Setting |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F205)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.01017)] [[CODE](https:\u002F\u002Fgithub.com\u002FQinbinLi\u002FFedKT)] |\n| Federated Model Distillation with Noise-Free Differential Privacy |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F216)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08310)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35184)] |\n| LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F217)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.15789)] |\n| Federated Learning with Fair Averaging. :fire: |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F223)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.14937)] [[CODE](https:\u002F\u002Fgithub.com\u002FWwZzz\u002FeasyFL)] |\n| H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F67)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00275)] |\n| Communication-efficient and Scalable Decentralized Federated Edge Learning. |  | IJCAI | 2021 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F720)] |\n| Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating | Xidian University; JD Tech | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17301)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00958)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947765\u002Fsecure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating)] |\n| FedRec++: Lossless Federated Recommendation with Explicit Feedback | SZU | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16546)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947798\u002Ffedrec-lossless-federated-recommendation-with-explicit-feedback)] |\n| Federated Multi-Armed Bandits | University of Virginia | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17156)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.12204)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenGroup\u002FFMAB)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38947985\u002Ffederated-multiarmed-bandits)] |\n| On the Convergence of Communication-Efficient Local SGD for Federated Learning | Temple University; University of Pittsburgh | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16920)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948341\u002Fon-the-convergence-of-communicationefficient-local-sgd-for-federated-learning)] |\n| FLAME: Differentially Private Federated Learning in the Shuffle Model | Renmin University of China; Kyoto University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17053)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08063)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948496\u002Fflame-differentially-private-federated-learning-in-the-shuffle-model)] [[CODE](https:\u002F\u002Fgithub.com\u002FRachelxuan11\u002FFLAME)] |\n| Toward Understanding the Influence of Individual Clients in Federated Learning | SJTU; The University of Texas at Dallas | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17263)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.10936)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948549\u002Ftoward-understanding-the-influence-of-individual-clients-in-federated-learning)] |\n| Provably Secure Federated Learning against Malicious Clients | Duke University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16849)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01854)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LP4uqW18yA0&ab_channel=PurdueCERIAS)] [[SLIDE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002FSecure_Federated_Learning.pdf)] |\n| Personalized Cross-Silo Federated Learning on Non-IID Data | Simon Fraser University; McMaster University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16960)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.03797)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948676\u002Fpersonalized-crosssilo-federated-learning-on-noniid-data)] [[UC.](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FPFL-Non-IID)] |\n| Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation | Cornell University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16669)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00753)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkpdonahue\u002Fmodel_sharing_games)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38948684\u002Fmodelsharing-games-analyzing-federated-learning-under-voluntary-participation)] |\n| Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning | University of Nevada; IBM Research | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17291)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.00655)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949098\u002Fcurse-or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning)] |\n| Game of Gradients: Mitigating Irrelevant Clients in Federated Learning | IIT Bombay; IBM Research | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17093)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.12257)] [[CODE](https:\u002F\u002Fgithub.com\u002Fnlokeshiisc\u002Fsfedavg-aaai21)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949109\u002Fgame-of-gradients-mitigating-irrelevant-clients-in-federated-learning)] [[SUPP](https:\u002F\u002Fgithub.com\u002Fnlokeshiisc\u002FSFedAvg-AAAI21)] |\n| Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models | CUHK; Arizona State University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17240)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13900)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949195\u002Ffederated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models)] [[CODE](https:\u002F\u002Fgithub.com\u002FREIYANG\u002FFedBCD)] |\n| Addressing Class Imbalance in Federated Learning | Northwestern University | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17219)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06217)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949283\u002Fadressing-class-imbalance-in-federated-learning)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbalanced-fl\u002FAddressing-Class-Imbalance-FL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F443009189)] |\n| Defending against Backdoors in Federated Learning with Robust Learning Rate | The University of Texas at Dallas | AAAI | 2021 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17118)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.03767)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38949344\u002Fdefending-against-backdoors-in-federated-learning-with-robust-learning-rate)] [[CODE](https:\u002F\u002Fgithub.com\u002FTinfoilHat0\u002FDefending-Against-Backdoors-with-Robust-Learning-Rate)] |\n| Free-rider Attacks on Model Aggregation in Federated Learning | Accenture Labs | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Ffraboni21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.11901)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27640)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Ffraboni21a\u002Ffraboni21a-supp.pdf)] |\n| Federated f-differential privacy | University of Pennsylvania | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fzheng21a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fenosair\u002Ffederated-fdp)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27595)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fzheng21a\u002Fzheng21a-supp.pdf)] |\n| Federated learning with compression: Unified analysis and sharp guarantees :fire: | The Pennsylvania State University; The University of Texas at Austin | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fhaddadpour21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.01154)] [[CODE](https:\u002F\u002Fgithub.com\u002FMLOPTPSU\u002FFedTorch)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27584)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fhaddadpour21a\u002Fhaddadpour21a-supp.pdf)] |\n| Shuffled Model of Differential Privacy in Federated Learning | UCLA; Google | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fgirgis21a.html)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27565)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fgirgis21a\u002Fgirgis21a-supp.pdf)] |\n| Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning | Google | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fcharles21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.05032)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27559)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fcharles21a\u002Fcharles21a-supp.pdf)] |\n| Federated Multi-armed Bandits with Personalization | University of Virginia; The Pennsylvania State University | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fshi21c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.13101)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenGroup\u002FPF_MAB)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27521)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fshi21c\u002Fshi21c-supp.pdf)] |\n| Towards Flexible Device Participation in Federated Learning | CMU; SYSU | AISTATS | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fruan21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.06954)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F27467)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv130\u002Fruan21a\u002Fruan21a-supp.pdf)] |\n| Federated Meta-Learning for Fraudulent Credit Card Detection |  | IJCAI | 2020 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F642)] [[VIDEO](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002Fvideo\u002F23994)] |\n| A Multi-player Game for Studying Federated Learning Incentive Schemes |  | IJCAI | 2020 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F769)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbenggggggggg\u002Ffedgame)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F353868739)] |\n| Practical Federated Gradient Boosting Decision Trees | NUS; UWA | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5895)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04206)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FPrivML)] |\n| Federated Learning for Vision-and-Language Grounding Problems | PKU; Tencent | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6824)] |\n| Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework | BUAA | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6096)] |\n| Federated Patient Hashing | Cornell University | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F6121)] |\n| Robust Federated Learning via Collaborative Machine Teaching | Symantec Research Labs; KAUST | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5826)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.02941)] |\n| FedVision: An Online Visual Object Detection Platform Powered by Federated Learning | WeBank | AAAI | 2020 | [[PUB](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F7021)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06202)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederatedAI\u002FFedVision)] |\n| FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization | UC Santa Barbara; UT Austin | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Freisizadeh20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.13014)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F7961)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Freisizadeh20a\u002Freisizadeh20a-supp.pdf)] |\n| How To Backdoor Federated Learning :fire: | Cornell Tech | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fbagdasaryan20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00459)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F8046)] [[CODE](https:\u002F\u002Fgithub.com\u002Febagdasa\u002Fbackdoor_federated_learning)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fbagdasaryan20a\u002Fbagdasaryan20a-supp.pdf)] |\n| Federated Heavy Hitters Discovery with Differential Privacy | RPI; Google | AISTATS | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fzhu20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.08534)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F8129)] [[SUPP](http:\u002F\u002Fproceedings.mlr.press\u002Fv108\u002Fzhu20a\u002Fzhu20a-supp.pdf)] |\n| Multi-Agent Visualization for Explaining Federated Learning | WeBank | IJCAI | 2019 | [[PUB](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F960)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FNPGf_OJrzOg)] |\n\n\u003C!-- 结束：fl-在顶级机器学习会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n\n## fl 在顶级机器学习会议和期刊中\n\n联邦学习相关论文被顶级机器学习（ML）会议和期刊接收，包括 [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fnips\u002Findex.html)（神经信息处理系统年度会议）、[ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficml\u002Findex.html)（国际机器学习大会）、[ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficlr\u002Findex.html)（国际表征学习大会）、[COLT](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fcolt\u002Findex.html)（计算学习理论年度会议）、[UAI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fuai\u002Findex.html)（人工智能不确定性会议）、[Machine Learning](https:\u002F\u002Fdblp.org\u002Fdb\u002Fjournals\u002Fml\u002Findex.html)、[JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fjmlr\u002Findex.html)（机器学习研究期刊）、[TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fpami\u002Findex.html)（IEEE模式分析与机器智能汇刊）。\n\n- [NeurIPS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANeurIPS%3A) [2024](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2024)（[OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2024\u002FConference#tab-accept-oral)），[2023](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2023)（[OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2023\u002FConference#tab-accept-oral)），[2022](https:\u002F\u002Fpapers.nips.cc\u002Fpaper_files\u002Fpaper\u002F2022)（[OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2022\u002FConference)），[2021](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021)（[OpenReview](https:\u002F\u002Fopenreview.net\u002Fgroup?id=NeurIPS.cc\u002F2021\u002FConference)），[2020](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020)，[2018](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018)，[2017](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017)\n- [ICML](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICML%3A) [2025](https:\u002F\u002Ficml.cc\u002FConferences\u002F2025\u002FSchedule?type=Poster)，[2024](https:\u002F\u002Ficml.cc\u002FConferences\u002F2024\u002FSchedule?type=Poster)，[2023](https:\u002F\u002Ficml.cc\u002FConferences\u002F2023\u002FSchedule?type=Poster)，[2022](https:\u002F\u002Ficml.cc\u002FConferences\u002F2022\u002FSchedule?type=Poster)，[2021](https:\u002F\u002Ficml.cc\u002FConferences\u002F2021\u002FSchedule?type=Poster)，[2020](https:\u002F\u002Ficml.cc\u002FConferences\u002F2020\u002FSchedule?type=Poster)，[2019](https:\u002F\u002Ficml.cc\u002FConferences\u002F2019\u002FSchedule?type=Poster)\n- [ICLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICLR%3A) [2025](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2025\u002FConference)，[2024](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2024\u002FConference)，[2023](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2023\u002FConference)，[2022](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2022\u002FConference)，[2021](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2021\u002FConference)，[2020](https:\u002F\u002Fopenreview.net\u002Fgroup?id=ICLR.cc\u002F2020\u002FConference)\n- [COLT](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3ACOLT%3A) [2023](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002F)\n- [UAI](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20venue%3AUAI%3A) [2025](https:\u002F\u002Fwww.auai.org\u002Fuai2025\u002Faccepted_papers)，[2024](https:\u002F\u002Fwww.auai.org\u002Fuai2024\u002Faccepted_papers)，[2023](https:\u002F\u002Fwww.auai.org\u002Fuai2023\u002Faccepted_papers)，[2022](https:\u002F\u002Fwww.auai.org\u002Fuai2022\u002Faccepted_papers)，[2021](https:\u002F\u002Fwww.auai.org\u002Fuai2021\u002Faccepted_papers)\n- [Machine Learning](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fml%3A) 2025、2024、2023、2022\n- [JMLR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fjmlr%3A) 2024（[v25](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv25\u002F)），2023（[v24](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F)），2021（[v22](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv22\u002F)）\n- [TPAMI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpami%3A) 2025、2024、2023、2022\n\n\u003Cdetails open>\n\u003Csummary>fl 在顶级机器学习会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：fl-在顶级机器学习会议和期刊中 -->\n\n|Title                                                           |    Affiliation                                                     |    Venue             |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | ---- | ------------------------------------------------------------ |\n| Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fzhang25f.html)] |\n| FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Ftang25a.html)] |\n| FeDCM: Federated Learning of Deep Causal Generative Models |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Frahman25a.html)] |\n| Federated Rényi Fair Inference in Federated Heterogeneous System |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fma25a.html)] |\n| FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Flin25a.html)] |\n| ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fkaragulyan25a.html)] |\n| FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fibrahim25a.html)] |\n| Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fdiana25a.html)] |\n| Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information |  | UAI | 2025 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv286\u002Fakgul25a.html)] |\n| Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off | SYSU | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=C7dmhyTDrx)] [[CODE](https:\u002F\u002Fgithub.com\u002F6lyc\u002FFedCEO_Collaborate-with-Each-Other)] |\n| Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wleRTUQj07)] |\n| SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j7H4mbeOI1)] [[CODE](https:\u002F\u002Fgithub.com\u002FNusIoraPrivacy\u002FSecEmb)] |\n| Causality Inspired Federated Learning for OOD Generalization |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pWWUJw2qew)] |\n| Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based \tStochastic Controlled Weight Averaging |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HqmXiuFaOr)] [[CODE](https:\u002F\u002Fgithub.com\u002FjunkangLiu0\u002FFedSWA)] |\n| One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PqJFVbJAMR)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingzhaoYang\u002FFedLMG)] |\n| FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XCLZgbm99O)] |\n| An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eLkkXaPFEP)] [[CODE](https:\u002F\u002Fgithub.com\u002F5Martina5\u002FESFMC)] |\n| Gap-Dependent Bounds for Federated $Q$-Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0n2nXmOxZS)] |\n| FedBEns: One-Shot Federated Learning based on Bayesian Ensemble |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oTCiv1bkjG)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjacopot96\u002FFedBEns)] |\n| NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hC7zCFk5Dp)] [[CODE](https:\u002F\u002Fgithub.com\u002FGabe-Thomp\u002Fntk-dfl)] |\n| Federated Learning for Feature Generalization with Convex Constraints |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pI4AbQ7pg1)] [[CODE](https:\u002F\u002Fgithub.com\u002Fskku-dhkim\u002FFedTorch.git)] |\n| Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EU5lci90fF)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdestiny301\u002Fuefl)] |\n| Towards Trustworthy Federated Learning with Untrusted Participants |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PjadKnUson)] |\n| Multi-Session Budget Optimization for Forward Auction-based Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bFB0N8ABIr)] |\n| Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0p86Mhg014)] [[CODE](https:\u002F\u002Fgithub.com\u002FMoratalYang\u002FFedDDA)] |\n| LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=li59703WbA)] |\n| Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cgHfR7bt0V)] |\n| Differentially Private Federated $k$-Means Clustering with Server-Side Data |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EFLPHl5RGJ)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjonnyascott\u002Ffed-dp-kmeans)] |\n| CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lvkVhZ776k)] |\n| Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MM6ZWF7gl9)] [[CODE](https:\u002F\u002Fgithub.com\u002FZLHe0\u002Ffedclup)] |\n| $S^2$FGL: Spatial Spectral Federated Graph Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pFQ3MnyIT6)] [[CODE](https:\u002F\u002Fgithub.com\u002FWonder7racer\u002FS2FGL.git)] |\n| FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HnwcrtoDd4)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsrijith1996\u002FFSL-SAGE)] |\n| Interaction-Aware Gaussian Weighting for Clustered Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dZAQxNFKGg)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fforum?id=XCLZgbm99O)] |\n| Efficient Heterogeneity-Aware Federated Active Data Selection |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pSdWTED0ZZ)] |\n| Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ny0m8YEUzH)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliaosunny123\u002FFedICU)] |\n| Rethinking the Temperature for Federated Heterogeneous Distillation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f9xsNQ8oSd)] |\n| FedClean: A General Robust Label Noise Correction for Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4kF2ZZcePc)] |\n| Federated Causal Structure Learning with Non-identical Variable Sets |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QlEx8f3S61)] |\n| FedECADO: A Dynamical System Model of Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gujuGnbhZr)] |\n| Efficient Federated Incomplete Multi-View Clustering |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sylDbssCU9)] [[CODE](https:\u002F\u002Fgithub.com\u002FTracesource\u002FEFIMVC)] |\n| Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7qvYLnJDRd)] [[CODE](https:\u002F\u002Fgithub.com\u002FPaddiHunter\u002FFIMCFG)] |\n| Local Pan-privacy for Federated Analytics |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M18dhHTFf8)] |\n| FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QwTDQXllam)] [[CODE](https:\u002F\u002Fgithub.com\u002FGanyuWang\u002FFedOne-BDPL)] |\n| Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zV5pkTMHPP)] [[CODE](https:\u002F\u002Fgithub.com\u002FHongyao-Chen\u002FHybridBN)] |\n| Private Federated Learning using Preference-Optimized Synthetic Data |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZuaU2bYzlc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmeiyuw\u002FPOPri)] |\n| Enhancing Foundation Models with Federated Domain Knowledge Infusion |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6SIVFmjIm4)] |\n| FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y7pDvbi9xz)] [[CODE](https:\u002F\u002Fgithub.com\u002FCYFang6\u002FFedPHA)] |\n| Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jwjvkWsePB)] [[CODE](https:\u002F\u002Fapp.box.com\u002Fs\u002Fphf6bhjy6owcr6b1rvfe412fiw059pxk)] |\n| Federated Node-Level Clustering Network with Cross-Subgraph Link Mending |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=38Nh0TebXZ)] |\n| Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mzPArjGqrs)] [[CODE](https:\u002F\u002Fgithub.com\u002Fallen4747\u002FFerret)] |\n| FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9hFQvmCl7P)] |\n| Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M7mVzCV6uU)] [[CODE](https:\u002F\u002Fgithub.com\u002FTerje-M\u002FFedGVI)] |\n| DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EvzArsKUww)] |\n| On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Eog0kXX7hW)] |\n| Safe-EF: Error Feedback for Non-smooth Constrained Optimization |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9D5aM5LQ3Y)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyardenas\u002Fsafe-ef)] |\n| Gradient Inversion of Multimodal Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j4IELrBhoG)] [[CODE](https:\u002F\u002Fgithub.com\u002FAlonZolfi\u002Fgi-dqa)] |\n| Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0p04srg7uf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkkhuge\u002FICML2025)] |\n| Decoupled SGDA for Games with Intermittent Strategy Communication |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZYkFTSEZ6k)] |\n| Private Model Personalization Revisited |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hw1kGPcSZ5)] |\n| Leveraging Randomness in Model and Data Partitioning for Privacy Amplification |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3K6BkFZ7ka)] |\n| Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XvOJvUlKc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpmangold\u002Fscaffold-speed-up)] |\n| Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hrBfufwMzg)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbuptcmm\u002Fphnhvvs)] |\n| FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V18WOxHRMq)] [[CODE](https:\u002F\u002Fgithub.com\u002Flxy66888\u002Ffedsmu.git)] |\n| One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=80mK2Mqaph)] [[CODE](https:\u002F\u002Fgithub.com\u002Fharrylee999\u002FFL-SAM)] |\n| Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dKfq3JbjnE)] |\n| Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XvF67vbCK)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzenghui9977\u002FFAFI_ICML25)] |\n| GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAk0ENu8LS)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiaruQian\u002FGHOST)] |\n| DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Nv6mOSqUVA)] |\n| BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fmlol78Qqf)] |\n| Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HdS6tZwwa7)] |\n| EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Bd9JlrqZhN)] [[CODE](https:\u002F\u002Fgithub.com\u002FZitongShi\u002FEAGLES)] |\n| Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Zn6hmmBnAa)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmahendrathapa\u002FpFedHB)] |\n| Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=R7gCixl2xR)] [[CODE](https:\u002F\u002Fgithub.com\u002FGivralNguyen\u002FFL-LDP-AMI)] |\n| Generalization in Federated Learning: A Conditional Mutual Information Framework |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kOttDCDYJp)] |\n| The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aooq3tQIX9)] [[CODE](https:\u002F\u002Fgithub.com\u002FLeopold1423\u002Ffedmud-icml25)] |\n| Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rCJNbDXkvC)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdcll-iiitd\u002FCoresetForVFL)] |\n| Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ULZHqJU4ZC)] |\n| Federated In-Context Learning: Iterative Refinement for Improved Answer Quality |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TUk7gCqtmf)] |\n| SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kjz03pmyW0)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenddHe0119\u002FSPMC)] |\n| You Get What You Give: Reciprocally Fair Federated Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZdmMDz33Io)] |\n| Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6znPjYn11w)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpupiu45\u002FFedGO)] |\n| Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning |  | ICML | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iBOMvaa2aN)] |\n| HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06804-2)] |\n| Fedflow: a personalized federated learning framework for passenger flow prediction |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06795-0)] |\n| Federated causal inference from observational data |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06819-9)] |\n| TransFed: cross-domain feature alignment for semi-supervised federated transfer learning |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06805-1)] |\n| Improve global generalization for personalized federated learning within a Stackelberg game |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-025-06770-9)] |\n| Efficient federated unlearning under plausible deniability |  | Mach Learn | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-024-06685-x)] [[CODE](https:\u002F\u002Fgithub.com\u002FAyush-Umu\u002FFederated-Unlearning-under-Plausible-Deniability)] |\n| Energy-based Backdoor Defense Against Federated Graph Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5Jc7r5aqHJ)] |\n| DEPT: Decoupled Embeddings for Pre-training Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vf5aUZT0Fz)] |\n| Subgraph Federated Learning for Local Generalization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cH65nS5sOz)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsung-won-kim\u002FFedLoG)] |\n| Problem-Parameter-Free Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZuazHmXTns)] |\n| Adaptive Gradient Clipping for Robust Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=03OkC0LKDD)] |\n| Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cznqgb4DNv)] |\n| LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PpYy0dR3Qw)] |\n| Group Distributionally Robust Dataset Distillation with Risk Minimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3JsU5QXNru)] |\n| GRAIN: Exact Graph Reconstruction from Gradients |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7bAjVh3CG3)] |\n| Towards Faster Decentralized Stochastic Optimization with Communication Compression |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CMMpcs9prj)] |\n| Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Bl3e8HV9xW)] |\n| Many-Objective Multi-Solution Transport |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Neb17mimVH)] |\n| Query-based Knowledge Transfer for Heterogeneous Learning Environments |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XKv29sMyjF)] |\n| Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ydREOIttdC)] |\n| Federated Granger Causality Learning For Interdependent Clients With State Space Representation |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KTgQGXz5xj)] |\n| Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=omrLHFzC37)] |\n| Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TrJ36UfD9P)] |\n| On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7pDI74iOyu)] |\n| PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B9kUJuWrYC)] |\n| Differentially Private Federated Learning with Time-Adaptive Privacy Spending |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W0nydevOlG)] |\n| Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zPDpdk3V8L)] |\n| Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5DUekOKWcS)] |\n| On the Byzantine-Resilience of Distillation-Based Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=of6EuHT7de)] |\n| Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sYNWqQYJhz)] |\n| Event-Driven Online Vertical Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FCBbh0HCrF)] |\n| On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BfUDZGqCAu)] |\n| Federated Domain Generalization with Data-free On-server Matching Gradient |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8TERgu1Lb2)] |\n| Unlocking the Potential of Model Calibration in Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Osr0KZJeTX)] |\n| FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6RjQ54M1rM)] |\n| Understanding the Stability-based Generalization of Personalized Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=znhZbonEoe)] |\n| Federated Residual Low-Rank Adaption of Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=e0rQRMUhs7)] |\n| FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=44hcrfzydU)] |\n| Vertical Federated Learning with Missing Features During Training and Inference |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OXi1FmHGzz)] |\n| Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FoUpv84hMw)] |\n| Selective Aggregation for Low-Rank Adaptation in Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iX3uESGdsO)] |\n| Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Equ277PBN0)] |\n| Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B8akWa62Da)] |\n| Debiasing Federated Learning with Correlated Client Participation |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9h45qxXEx0)] |\n| Decoupled Subgraph Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=v1rFkElnIn)] |\n| Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=79nO2DPjVX)] |\n| Towards Federated RLHF with Aggregated Client Preference for LLMs |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mqNKiEB6pd)] |\n| SparsyFed: Sparse Adaptive Federated Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OBUQNASaWw)] |\n| Can Textual Gradient Work in Federated Learning? |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Cy5IKvYbR3)] |\n| Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xiDJaTim3P)] |\n| Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3wEGdrV5Cb)] |\n| Connecting Federated ADMM to Bayes |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ipQrjRsl11)] |\n| Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ROpY0qRUXL)] |\n| Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f65RuQgVlp)] |\n| Federated Few-Shot Class-Incremental Learning |  | ICLR | 2025 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZiPoAlKf9Y)] |\n| Re-Fed+: A Better Replay Strategy for Federated Incremental Learning |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10930690)] |\n| DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10908045)] |\n| Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10833756)] |\n| Federated Multi-View K-Means Clustering |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10810504)] |\n| Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10696955)] |\n| Medical Federated Model With Mixture of Personalized and Shared Components |  | TPAMI | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10697408)] |\n| FedAST: Federated Asynchronous Simultaneous Training |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Faskin24a.html)] |\n| On Convergence of Federated Averaging Langevin Dynamics |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fdeng24a.html)] |\n| On the Convergence of Hierarchical Federated Learning with Partial Worker Participation |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fjiang24a.html)] |\n| Pure Exploration in Asynchronous Federated Bandits |  | UAI | 2024 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv244\u002Fwang24c.html)] |\n| One-shot Federated Learning via Synthetic Distiller-Distillate Communication |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6292sp7HiE)] |\n| Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uO53206oLJ)] |\n| FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=c3OZBJpN7M)] |\n| Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6lx34fpanw)] |\n| Federated Model Heterogeneous Matryoshka Representation Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5yboFMpvHf)] |\n| Federated Graph Learning for Cross-Domain Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=UBpPOqrBKE)] |\n| FedGMark: Certifiably Robust Watermarking for Federated Graph Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xeviQPXTMU)] |\n| Dual-Personalizing Adapter for Federated Foundation Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nkwPiBSw1f)] |\n| Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DUFD6vsyF8)] |\n| Taming the Long Tail in Human Mobility Prediction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wT2TIfHKp8)] |\n| Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EVw8Jh5Et9)] |\n| Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=55zLbH7dE1)] |\n| DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FDfrPugkGU)] |\n| Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DLNOBJa7TM)] |\n| Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FqWyzyErVT)] |\n| FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bMbteQRhDI)] |\n| Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nw6ANsC66G)] |\n| FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TcCorXxNJQ)] |\n| Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6SRPizFuaE)] |\n| pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xW6ga9i4eA)] |\n| Why Go Full? Elevating Federated Learning Through Partial Network Updates |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6OK8Qy9yVu)] |\n| FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E7fZOoiEKl)] |\n| FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I96GFYalFO)] |\n| Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3YIyB82rjX)] |\n| A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=h1iMVi2iEM)] |\n| Private and Personalized Frequency Estimation in a Federated Setting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0nzKznCjFG)] |\n| The Sample-Communication Complexity Trade-off in Federated Q-Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6YIpvnkjUK)] |\n| Federated Ensemble-Directed Offline Reinforcement Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ypaqE8UwsC)] |\n| Federated Black-Box Adaptation for Semantic Segmentation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Fp3JVz5XE7)] |\n| Thinking Forward: Memory-Efficient Federated Finetuning of Language Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dGQtja9X2C)] |\n| Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Y4L8GQXZZO)] |\n| Optimal Design for Human Preference Elicitation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cCGWj61Ael)] |\n| Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y6JotynERr)] |\n| Personalized Federated Learning via Feature Distribution Adaptation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wl2optQcng)] |\n| SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HeJ1cBAgiV)] |\n| A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hilGwNabqB)] |\n| RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=js74ZCddxG)] |\n| FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QXkFC7D6p4)] |\n| End-to-end Learnable Clustering for Intent Learning in Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=As91fJvY9E)] |\n| FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I3IuclVLFZ)] |\n| Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HS0faHRhWD)] |\n| FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D6MQrw9HFu)] |\n| A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3YkeHuT1o6)] |\n| FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zBMKodNgKX)] |\n| Low Precision Local Training is Enough for Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vvpewjtnvm)] |\n| Resource-Aware Federated Self-Supervised Learning with Global Class Representations |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Of4iNAIUSe)] |\n| On the Necessity of Collaboration for Online Model Selection with Decentralized Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uqWfLgZpV1)] |\n| The Power of Extrapolation in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FuTfZK7PK3)] |\n| (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lflwtGE6Vf)] |\n| On Sampling Strategies for Spectral Model Sharding |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PgTHgLUFi3)] |\n| Customizing Language Models with Instance-wise LoRA for Sequential Recommendation |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=isZ8XRe3De)] |\n| SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dAXuir2ets)] |\n| HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6LVxO1C819)] |\n| Stabilized Proximal-Point Methods for Federated Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WukSyFSzDt)] |\n| DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Pezt0xttae)] |\n| Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g8wnC1E1OS)] |\n| Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j6Zsoj544N)] |\n| FedAvP: Augment Local Data via Shared Policy in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M1PRU0x1Iz)] |\n| CoBo: Collaborative Learning via Bilevel Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SjQ1iIqpfU)] |\n| Convergence Analysis of Split Federated Learning on Heterogeneous Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ud0RBkdBfE)] |\n| Communication-Efficient Federated Group Distributionally Robust Optimization |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xNZEjFe0mh)] |\n| Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YxyYTcv3hp)] |\n| Federated Learning over Connected Modes |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JL2eMCfDW8)] |\n| Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yvUHnBkCzd)] |\n| Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1kyc4TSOFZ)] |\n| Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=T826pwZLci)] |\n| DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=792txRlKit)] |\n| Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5FHzrRGOKR)] |\n| Hierarchical Federated Learning with Multi-Timescale Gradient Correction |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aCAb1qNXI0)] |\n| HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3ie8NWA1El)] |\n| SPEAR: Exact Gradient Inversion of Batches in Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lPDxPVS6ix)] |\n| Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WftaVkL6G2)] |\n| Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GVlJVX3iiq)] |\n| Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=G89r8Mgi5r)] |\n| Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0LfgE6kvKZ)] |\n| Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MwJo3zuiTm)] |\n| Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6ejpSVIiIl)] |\n| Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HhnpPISAUH)] |\n| FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JiRGxrqHh0)] |\n| Active preference learning for ordering items in- and out-of-sample |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PSLH5q7PFo)] |\n| Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gkOzoHBXUw)] |\n| Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WBLPlszJI5)] |\n| Revisiting Ensembling in One-Shot Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7rWTS2wuYX)] |\n| FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=djGx0hucok)] |\n| $\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I79q7wIRkS)] |\n| FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection |  | NeurIPS | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rovpCs3ZEO)] |\n| Momentum Approximation in Asynchronous Private Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pEpjKicxFk)] |\n| Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8TrYvsbw1f)] |\n| Federated Learning with Generative Content |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hMbgXHjWrg)] |\n| Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pxP2M3xiE6)] |\n| Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1JGa1OIRjQ)] |\n| Defection-Free Collaboration between Competitors in a Learning System |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2Sd5xNv1sm)] |\n| On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Eph8dS188u)] |\n| EncCluster: Bringing Functional Encryption in Federated Foundational Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7bgJ7t5kkW)] |\n| Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SXMsg44Znz)] |\n| Hot Pluggable Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FazIrAXoM6)] |\n| Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MxgmAil8ud)] |\n| The Future of Large Language Model Pre-training is Federated |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hfeH5AP9NY)] |\n| Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jNZEIQsJes)] |\n| The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RoUUV2wLdn)] |\n| ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zpEQUbYZPc)] |\n| DeComFL: Federated Learning with Dimension-Free Communication |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Vy9ltlTXXd)] |\n| Improving Group Connectivity for Generalization of Federated Deep Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vGyB8PVl4C)] |\n| MAP: Model Merging with Amortized Pareto Front Using Limited Computation |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KfOdVp4pfm)] |\n| OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qQdPSuW7qx)] |\n| Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OxpWu6J0TW)] |\n| Worldwide Federated Training of Language Models |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YMSLZUmQVV)] |\n| FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uBooD9HQQu)] |\n| Enhancing Causal Discovery in Federated Settings with Limited Local Samples |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Js64okXDUE)] |\n| $\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6WNNB9TaVw)] |\n| DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing |  | NeurIPS workshop | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GdzTE7eruH)] |\n| FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization |  | JMLR | 2024 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv25\u002F23-0764.html)] |\n| Effective Federated Graph Matching |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rSfzchjIYu)] |\n| Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zwUEk9WpsR)] |\n| Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2zLt2Odckx)] |\n| FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AoYhtJ4A90)] |\n| Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=p0MGN0LSnx)] |\n| A New Theoretical Perspective on Data Heterogeneity in Federated Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=re6es2atbl)] |\n| Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models |  | ICML | 2024 | [[](https:\u002F\u002Fopenreview.net\u002Fforum?id=QgvBcOsF4B)] |\n| Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g43yUNWX4V)] |\n| Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Izv7gBnap3)] |\n| Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yHRxnhKyEJ)] |\n| Accelerating Federated Learning with Quick Distributed Mean Estimation |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gWEwIlZrbQ)] |\n| Fair Federated Learning via the Proportional Veto Core |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6Zgjrowepn)] |\n| AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PHUAG63Efe)] |\n| Recovering Labels from Local Updates in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E41gvBG4s6)] |\n| FedMBridge: Bridgeable Multimodal Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jrHUbftLd6)] |\n| Harmonizing Generalization and Personalization in Federated Prompt Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YYwERRXsJW)] |\n| Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6axTFAlzRV)] |\n| Accelerating Heterogeneous Federated Learning with Closed-form Classifiers |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cMige5MK1N)] |\n| Federated Combinatorial Multi-Agent Multi-Armed Bandits |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lrFwPeDdEQ)] |\n| A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GentO2E4ID)] |\n| Private Heterogeneous Federated Learning  Without a Trusted Server Revisited: Error-Optimal and  Communication-Efficient Algorithms for Convex Losses |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sSAEhcdB9N)] |\n| FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kc4dZYJlJG)] |\n| Pursuing Overall Welfare in Federated Learning through Sequential Decision Making |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=foPMkomvk1)] |\n| PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3WCvnkHnxV)] |\n| Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k2axqNsVVO)] |\n| Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mNzkumTSVL)] |\n| Federated Optimization with Doubly Regularized Drift Correction |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JD03zxWZzs)] |\n| FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0nMzOmkBHC)] |\n| Certifiably Byzantine-Robust Federated Conformal Prediction |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4axAQHwBOE)] |\n| Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vQmVmMN5ft)] |\n| Clustered Federated Learning via Gradient-based Partitioning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=J4HJUF70qm)] |\n| Recurrent Early Exits for Federated Learning with Heterogeneous Clients |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=w4B42sxNq3)] |\n| Rethinking the Flat Minima Searching in Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6TM62kpI5c)] |\n| FedBAT: Communication-Efficient Federated Learning via Learnable Binarization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=x2zxPwCkAZ)] |\n| Federated Representation Learning in the Under-Parameterized Regime |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LIQYhV45D4)] |\n| FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=akyElNlUVA)] |\n| Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wuQ2DRPAuy)] |\n| SILVER: Single-loop variance reduction and application to federated learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pOgMluzEIH)] |\n| SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zEqeNEuiJr)] |\n| FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XecUTmB9yD)] |\n| Federated Continual Learning via Prompt-based Dual Knowledge Transfer |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kqa5JakTjB)] |\n| Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cit0hg4sEz)] |\n| Decomposable Submodular Maximization in Federated Setting |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SAbZExIIgG)] |\n| Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sTVSyqD6XX)] |\n| Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=01M0N8VgfB)] |\n| Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often! |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ffS0aYP6mk)] |\n| Byzantine Resilient and Fast Federated Few-Shot Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=q5q59s2WJy)] |\n| Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kbd9A4lVoX)] |\n| Ranking-based Client Imitation Selection for Efficient Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FMEhnS0948)] |\n| Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kVgpa1rfLO)] |\n| FADAS: Towards Federated Adaptive Asynchronous Optimization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j56JAd29uH)] |\n| Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LIPGadocTe)] |\n| FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wjq2bS7fTK)] |\n| MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Jvh8HM9YEJ)] |\n| Federated Neuro-Symbolic Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EQXZqBXeW9)] |\n| Adaptive Group Personalization for Federated Mutual Transfer Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DqC9XiI71U)] |\n| Balancing Similarity and Complementarity for Federated Learning |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=v6tAdeCXKH)] |\n| Federated Self-Explaining GNNs with Anti-shortcut Augmentations |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZxDqSBgFSM)] |\n| A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NkN6wrYXe5)] |\n| COALA: A Practical and Vision-Centric Federated Learning Platform |  | ICML | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ATRnM8PyQX)] |\n| Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization |  | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-024-06541-y)] |\n| Communication-efficient clustered federated learning via model distance | USTC; State Key Laboratory of Cognitive Intelligence | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06443-5)] |\n| Federated learning with superquantile aggregation for heterogeneous data. | Google Research | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06332-x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.09429)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkrishnap25\u002Fsimplicial-fl)] |\n| Aligning model outputs for class imbalanced non-IID federated learning | NJU | Mach Learn | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06241-5)] |\n| Federated Learning of Generalized Linear Causal Networks |  | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10480288)] |\n| Cross-Modal Federated Human Activity Recognition |  | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10440498)] |\n| Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling | Northeastern University; UoM | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10402074)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14008)] [[CODE](https:\u002F\u002Fgithub.com\u002FUMDataScienceLab\u002FFederated_Gaussian_Process)] |\n| The Impact of Adversarial Attacks on Federated Learning: A Survey | IIT | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10274102)] |\n| Understanding and Mitigating Dimensional Collapse in Federated Learning | NUS | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10336535)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00226)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbytedance\u002FFedDecorr)] |\n| No One Left Behind: Real-World Federated Class-Incremental Learning | CAS; UCAS | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10323204)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00903)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiahuaDong\u002FLGA)] |\n| Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning | WHU | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10295990)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.16286)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FFCCL)] |\n| Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy | HPU; XJTU | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10316599)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.07902)] [[CODE](https:\u002F\u002Fgithub.com\u002FIoTDATALab\u002FMAPA)] |\n| A Bayesian Federated Learning Framework With Online Laplace Approximation | SUSTech | TPAMI | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10274722)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01936)] [[CODE](https:\u002F\u002Fgithub.com\u002FKlitter\u002FA-Bayesian-Federated-Learning-Framework-with-Online-Laplace-Approximation)] |\n| Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting | USTC; HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tm8s3696Ox)] |\n| One-shot Empirical Privacy Estimation for Federated Learning | Google | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0BqyZSWfzo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03098)] |\n| Stochastic Controlled Averaging for Federated Learning with Communication Compression | LinkedIn; UPenn | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jj5ZjZsWJe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.08165)] |\n| A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging | IBM | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZKEuFKfCKA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03401)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002Ffedau)] |\n| A Mutual Information Perspective on Federated Contrastive Learning | QualComm | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JrmPG9ufKg)] |\n| Benchmarking Algorithms for Federated Domain Generalization | Purdue University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wprSv7ichW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.04942)] [[CODE](https:\u002F\u002Fgithub.com\u002Finouye-lab\u002FFedDG_Benchmark)] |\n| Effective and Efficient Federated Tree Learning on Hybrid Data | UC Berkeley | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=py4ZV2qYQI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11865)] |\n| Federated Recommendation with Additive Personalization | UTS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xkXdE81mOK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09109)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmtics\u002FFedRAP)] |\n| Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration | PSU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4aywmeb97I)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=4aywmeb97I&name=supplementary_material)] |\n| Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning | USC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAs4LdaP9Y)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nAs4LdaP9Y&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.01289)] |\n| Accurate Forgetting for Heterogeneous Federated Continual Learning | THU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ShQrnAsbPI)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FAF-FCL-7D65)] |\n| Federated Causal Discovery from Heterogeneous Data | MBZUAI | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=m7tJxajC3G)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.13241)] [[CODE](https:\u002F\u002Fgithub.com\u002Flokali\u002FFedCDH)] |\n| On Differentially Private Federated Linear Contextual Bandits | Wayne State University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cuAxSHcsSX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=cuAxSHcsSX&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.13945)] |\n| Incentivized Truthful Communication for Federated Bandits | University of Virginia | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ykEixGIJYb)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.04485)] |\n| Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting | UIUC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6J3ehSUrMU)] |\n| FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity | KAUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hbHwZYqk9T)] |\n| Text-driven Prompt Generation for Vision-Language Models in Federated Learning | Robert Bosch LLC | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NW31gAylIm)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.06123)] |\n| Improving LoRA in Privacy-preserving Federated Learning | Northeastern University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NLPzL6HWNl)] |\n| FedWon: Triumphing Multi-domain Federated Learning Without Normalization | Sony AI | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hAYHmV1gM8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05879)] |\n| FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning | TU Delft | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DRu8PMHgCh)] |\n| FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler | ANL; UIUC; NCSA | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=msXxrttLOi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.14675)] [[CODE](https:\u002F\u002Fgithub.com\u002FAPPFL\u002FFedCompass)] [[PAGE](https:\u002F\u002Fappfl.github.io\u002FFedCompass)] |\n| Bayesian Coreset Optimization for Personalized Federated Learning | IIT Bombay | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uz7d2N2zul)] |\n| Layer-wise linear mode connectivity | Ruhr-Universtät Bochum | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LfmZh91tDI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.06966)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=LfmZh91tDI&name=supplementary_material)] |\n| Fake It Till Make It: Federated Learning with Consensus-Oriented Generation | SJTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NY3wMJuaLf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05966)] |\n| Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning | INSAIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=krx55l2A6G)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=krx55l2A6G&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03013)] |\n| Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning | Columbia University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D2eOVqPX9g)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.15273)] |\n| Adaptive Federated Learning with Auto-Tuned Clients | Rice University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g0mlwqs8pi)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=g0mlwqs8pi&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.11201)] |\n| Backdoor Federated Learning by Poisoning Backdoor-Critical Layers | ND | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AJBGSVSTT2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AJBGSVSTT2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.04466)] |\n| Federated Q-Learning: Linear Regret Speedup with Low Communication Cost | PSU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fe6ANBxcKM)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=fe6ANBxcKM&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.15023)] |\n| FedImpro: Measuring and Improving Client Update in Federated Learning | HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=giU9fYGTND)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.07011)] |\n| Federated Wasserstein Distance | MIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rsg1mvUahT)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rsg1mvUahT&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01973)] |\n| An improved analysis of per-sample and per-update clipping in federated learning | DTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BdPvGRvoBC)] |\n| FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation | NTU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nbPGqeH3lt)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nbPGqeH3lt&name=supplementary_material)] |\n| Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning | HKU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Cc0qk6r4Nd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.11464)] |\n| Momentum Benefits Non-iid Federated Learning Simply and Provably | PKU; UPenn | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TdhkAcXkRi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.16504)] |\n| Communication-Efficient Federated Non-Linear Bandit Optimization | Yale University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nFI3wFM9yN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.01695)] |\n| Fair and Efficient Contribution Valuation for Vertical Federated Learning | Huawei | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sLQb8q0sUi)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=sLQb8q0sUi&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.02658)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenanf\u002FVerFedLogistic.jl)] |\n| Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SBj2Qdhgew)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11333)] |\n| Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients | PolyU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8FHWkY0SwF)] |\n| PeFLL: Personalized Federated Learning by Learning to Learn | IST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=MrYiwlDRQO)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=MrYiwlDRQO&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05515)] |\n| Communication-Efficient Gradient  Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates | JHU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hORCalGn3Z)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=hORCalGn3Z&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05100)] |\n| FedInverse: Evaluating Privacy Leakage in Federated Learning | USQ | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nTNgkEIfeb)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nTNgkEIfeb&name=supplementary_material)] |\n| FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kjn99xFUF3)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=kjn99xFUF3&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06103)] |\n| Robust Training of Federated Models with Extremely Label Deficiency | HKBU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qxLVaYbsSI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.14430)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvisitworld123\u002FTwin-sight)] |\n| Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory | RIKEN AIP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EcetCr4trp)] |\n| Teach LLMs to Phish: Stealing Private Information from Language Models | Princeton University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qo21ZlfNu6)] |\n| Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rM9VJPB20F)] |\n| Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise | HKUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CIqjp9yTDq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.14567)] |\n| Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks | CAS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=s8cMuxI5gu)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=s8cMuxI5gu&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03124)] [[CODE](https:\u002F\u002Fgithub.com\u002Fybwang119\u002Flabel_recovery)] |\n| Local Composite Saddle Point Optimization | Purdue University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kklwv4c4dI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15643)] |\n| Enhancing Neural Training via a Correlated Dynamics Model | TIIT | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=c9xsaASm9L)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13247)] |\n| EControl: Fast Distributed Optimization with Compression and Error Control | Saarland University | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lsvlvWB9vz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=lsvlvWB9vz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05645)] |\n| Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit | HKUST | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=m52uU0dVbH)] |\n| FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent | UMCP | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kl9CqKf7h6)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Kl9CqKf7h6&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03156)] [[CODE](https:\u002F\u002Fgithub.com\u002FATP-1010\u002FFedHyper)] |\n| Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate | CUHK | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7pWRLDBAtc)] |\n| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | University of Cambridge | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zzqn5G9fjn)] |\n| Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1ii8idH4tH)] |\n| VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | THU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sqRgz88TM3)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09827)] [[CODE](https:\u002F\u002Fgithub.com\u002FFLAIR-THU\u002FVFLAIR)] |\n| Incentive-Aware Federated Learning with Training-Time Model Rewards | NUS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FlY7WQ2hWS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FlY7WQ2hWS&name=supplementary_material)] |\n| VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | NUS | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=glwwbaeKm2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.02040)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FVertiBench)] |\n| FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | ZJU | ICLR | 2024 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V3j5d0GQgH)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=V3j5d0GQgH&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.08977)] |\n| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZdxGmJGKOo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19442)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ZdxGmJGKOo&name=supplementary_material)] |\n| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yKCLfOOIL7)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06351)] |\n| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=n3fPDW87is)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13591)] [[CODE](https:\u002F\u002Fgithub.com\u002FGeovaniRizk\u002FRobust-Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity)] |\n| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9OqezkNxnX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9OqezkNxnX&name=supplementary_material)] |\n| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Dxhv8Oja2V)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.03154)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliyipeng00\u002Fconvergence)] |\n| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LGKxz9clGG)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15165)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsarapieri\u002Ffed_het)] |\n| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance | JHU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rzDBoh1tBh)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rzDBoh1tBh&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09396)] |\n| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Rutgers University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=46x3zvYCyQ)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=46x3zvYCyQ&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13024)] |\n| Incentivized Communication for Federated Bandits | University of Virginia | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1aQivXgZKj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.11702)] |\n| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Northeastern University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M6UccKMFGl)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.12600)] |\n| IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Vanderbilt University; VinUniversity | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cemEOP8YoC)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=cemEOP8YoC&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsail-research\u002Fiba)] |\n| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning | KAIST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=P3Z59Okb5I)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=P3Z59Okb5I&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07485)] |\n| Federated Linear Bandits with Finite Adversarial Actions | University of Virginia | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bzXpQUnule)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=bzXpQUnule&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00973)] |\n| FedNAR: Federated Optimization with Normalized Annealing Regularization | MBZUAI | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=x5fs7TXKDc)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=x5fs7TXKDc&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03163)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljb121002\u002Ffednar)] |\n| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Concordia University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HRGd5dcVfw)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=HRGd5dcVfw&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03937)] [[CODE](https:\u002F\u002Fgithub.com\u002FGwenLegate\u002FGuidingLastLayerFLPretrain)] |\n| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | HZAU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0ycX03sMAT)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=0ycX03sMAT&name=supplementary_material)] |\n| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection | KAIST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2D7ou48q0E)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2D7ou48q0E&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17097)] [[CODE](https:\u002F\u002Fgithub.com\u002FKthyeon\u002Fssfod)] |\n| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | USC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3b9sqxCW1x)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07784)] [[CODE](https:\u002F\u002Fgithub.com\u002FSaraBabakN\u002FMFCL-NeurIPS23)] |\n| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | UTS | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qJJmu4qsLO)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qJJmu4qsLO&name=supplementary_material)] |\n| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning | Rice University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KMxRQO7P98)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=KMxRQO7P98&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Flzcemma\u002FRACE_Distance)] |\n| Lockdown: Backdoor Defense for Federated Learning  with Isolated Subspace Training | Gatech | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=V5cQH7JbGo)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=V5cQH7JbGo&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002FLockdown)] |\n| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | PSU; UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nX0zYBGEka)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nX0zYBGEka&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FFedGame)] |\n| Towards Personalized Federated Learning via Heterogeneous Model Reassembly | PSU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zpVCITHknd)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=zpVCITHknd&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.08643)] [[CODE](https:\u002F\u002Fgithub.com\u002FJackqqWang\u002FpfedHR)] |\n| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction | GWU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AWpWaub6nf)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AWpWaub6nf&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08670)] |\n| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | ECNU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3H9QH1v6U9)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3H9QH1v6U9&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13546)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FDFRD-0C83\u002F)] |\n| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning | Western University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AYiRHZirD2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AYiRHZirD2&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FGanyuWang\u002FVFL-CZOFO)] |\n| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Xidian University; University of Guelph; Zhejiang Key Laboratory of Multi-dimensional Perception Technology, Application and Cybersecurity | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3n8PNUdvSg)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3n8PNUdvSg&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.05431)] |\n| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | SJTU; Shanghai AI Laboratory | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wwmKVO8bsR)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=wwmKVO8bsR&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedGELA)] |\n| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | GMU; SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Yq6GKgN3RC)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Yq6GKgN3RC&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002Fepisode_plusplus)] |\n| FedL2P: Federated Learning to Personalize | University of Cambridge; Samsung AI Center | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FM81CI68Iz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FM81CI68Iz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02420)] [[CODE](https:\u002F\u002Fgithub.com\u002Froyson\u002Ffedl2p\u002F)] |\n| Adaptive Test-Time Personalization for Federated Learning | UIUC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rbw9xCU6Ci)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18816)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002FATP)] |\n| Federated Conditional Stochastic Optimization | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E0Gw1uz7lU)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=E0Gw1uz7lU&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02524)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FFederated-Minimax-and-Conditional-Stochastic-Optimization\u002Ftree\u002Fmain)] |\n| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RW7rZ8Y3Bp)] |\n| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EcmqyXekuP)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=EcmqyXekuP&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12534)] |\n| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ody3RBUuJS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ody3RBUuJS&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12433)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyh-yao\u002FFedGCN)] |\n| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OlSTwlz96r)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=OlSTwlz96r&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09866)] |\n| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rG1M3kOVba)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rG1M3kOVba&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.02623)] [[CODE](https:\u002F\u002Fgithub.com\u002Fiwang05\u002FFLuID)] |\n| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=j4QVhftpYM)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=j4QVhftpYM&name=supplementary_material)] |\n| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B5XwENgy0T)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=B5XwENgy0T&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06701)] |\n| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IjZa2fQ8tL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.00227)] |\n| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | The University of Sydney | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ylPX5D7It7)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ylPX5D7It7&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05706)] |\n| DELTA: Diverse Client Sampling for Fasting Federated Learning | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6XC5iKqRVm)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=6XC5iKqRVm&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13925)] |\n| Federated Compositional Deep AUC Maximization | Temple University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tF7W8ai8J3)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=tF7W8ai8J3&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10101)] |\n| A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning | PSU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=S6ajVZy6FA)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=S6ajVZy6FA&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhfzhang31\u002FA3FL)] |\n| Flow: Per-instance Personalized Federated Learning | University of Massachusetts | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BI031mw7iS)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=BI031mw7iS&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.15281)] [[CODE](https:\u002F\u002Fgithub.com\u002FAstuary\u002FFlow)] |\n| Eliminating Domain Bias for Federated Learning in Representation Space | SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nO5i1XdUS0)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nO5i1XdUS0&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.14975)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FDBE)] |\n| Federated Learning with Manifold Regularization and Normalized Update Reaggregation | BIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7uPnuoYqac)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7uPnuoYqac&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05924)] |\n| Structured Federated Learning through Clustered Additive Modeling | University of Technology Sydney | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2XT3UpOv48)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2XT3UpOv48&name=supplementary_material)] |\n| Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | ZJU; Singapore University of Technology and Design | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gJewjFjfN2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gJewjFjfN2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07587)] [[CODE](https:\u002F\u002Fgithub.com\u002FZackZikaiXiao\u002FFedGraB)] |\n| Dynamic Personalized Federated Learning with Adaptive Differential Privacy | WHU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RteNLuc8D9)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=RteNLuc8D9&name=supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxiyuanyang45\u002FDynamicPFL)] |\n| Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning | ShanghaiTech University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dEDdRWunxU)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=dEDdRWunxU&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.13923)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhyczy\u002FFed-CO2)] |\n| Solving a Class of Non-Convex  Minimax Optimization in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SpStmVboGy)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SpStmVboGy&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03613)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FFederated-Minimax-and-Conditional-Stochastic-Optimization\u002F)] |\n| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VNyKBipt91)] [[CODE](https:\u002F\u002Fgithub.com\u002Finsujeon\u002FMetaVD)] |\n| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=0ORqsMY6OL)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=0ORqsMY6OL&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19807)] |\n| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | NTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tmxjuIFSEc)] [[CODE](https:\u002F\u002Fgithub.com\u002Fculiver\u002FSPACE)] |\n| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | PKU; Tencent | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=txPdKZrrZF)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=txPdKZrrZF&name=supplementary_material)] |\n| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | BUAA; HKBU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=phnGilhPH8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.05077)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvisitworld123\u002Ffedfed)] |\n| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning | SCU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kuxu4lCRr5)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=kuxu4lCRr5&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.09183)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbdemo\u002Fpfedbred_public)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F661506638)] |\n| Spectral Co-Distillation for Personalized Federated Learning | SUTD | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=RqjQL08UFc)] |\n| Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy | ZJU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4ZaPpVDjGQ)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=4ZaPpVDjGQ&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09624)] |\n| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7ETbK9lQd7)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7ETbK9lQd7&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04924)] [[CODE](https:\u002F\u002Fgithub.com\u002FBerivanIsik\u002Frrsc)] |\n| (Amplified) Banded Matrix Factorization: A unified approach to private training | Google DeepMind | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zEm6hF97Pz)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=zEm6hF97Pz&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.08153)] |\n| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | KIT | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nXNsqB4Yr1)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=nXNsqB4Yr1&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17005)] [[CODE](https:\u002F\u002Fgithub.com\u002Fk1l1\u002FSLT)] |\n| Privacy Amplification via Compression:  Achieving the Optimal Privacy-Accuracy-Communication Trade-off in  Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=izNfcaHJk0)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=izNfcaHJk0&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01541)] |\n| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zurich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=g2ROKOASiv)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=g2ROKOASiv&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16272)] |\n| Resilient Constrained Learning | UPenn | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=h0RVoZuUl6)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=h0RVoZuUl6&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02426)] |\n| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | KAUST | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=loxinzXlCx)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=loxinzXlCx&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15580)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmysteryresearcher\u002Fdasha-partial-participation)] |\n| Collaboratively Learning Linear Models with Structured Missing Data | Stanford University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=waDF0oACu2)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=waDF0oACu2&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11947)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryxcheng\u002Fcollab)] |\n| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | EPFL | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qCglMj6A4z)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qCglMj6A4z&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.01463)] |\n| Fast Optimal Locally Private Mean Estimation via Random Projections | Apple Inc. | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=K3JgUvDSYX)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=K3JgUvDSYX&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04444)] [[CODE](https:\u002F\u002Fgithub.com\u002Fapple\u002Fml-projunit)] |\n| Contextual Stochastic Bilevel Optimization | EPFL; ETH Zürich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SHBksHKutP)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SHBksHKutP&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18535)] |\n| Understanding Deep Gradient Leakage via Inversion Influence Functions | MSU; Michigan State University; University of Texas at Austin | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tBib2fWr3r)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=tBib2fWr3r&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13016)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002Finversion-influence-function)] |\n| Inner Product-based Neural Network Similarity | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9eneYFIGKq)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9eneYFIGKq&name=supplementary_material)] |\n| Correlation Aware Sparsified Mean Estimation Using Random Projection | CMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VacSQpbI0U)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=VacSQpbI0U&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18868)] [[CODE](https:\u002F\u002Fgithub.com\u002F11hifish\u002FRand-Proj-Spatial)] |\n| TIES-Merging: Resolving Interference When Merging Models | UNC | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xtaX3WyCj1)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=xtaX3WyCj1&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.01708)] [[CODE](https:\u002F\u002Fgithub.com\u002Fprateeky2806\u002Fties-merging)] |\n| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Purdue University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qyixBZl8Ph)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qyixBZl8Ph&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.04792)] [[CODE](https:\u002F\u002Fgithub.com\u002Faparna-aketi\u002Fglobal_update_tracking)] |\n| Large-Scale Distributed Learning via Private On-Device LSH | UMD | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dpdbbN7AKr)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=dpdbbN7AKr&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02563)] |\n| Faster Relative Entropy Coding with Greedy Rejection Coding | University of Cambridge | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KXbAgvLi2l)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=KXbAgvLi2l&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.15746)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcambridge-mlg\u002Ffast-rec-with-grc)] |\n| Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization | SJTU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gVLKXT9JwG)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gVLKXT9JwG&name=supplementary_material)] |\n| Momentum Provably Improves Error Feedback! | ETH AI Center; ETH Zurich | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1h92PmnKov)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=1h92PmnKov&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15155)] |\n| Strategic Data Sharing between Competitors | Sofia University | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AkK3S2spZs)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=AkK3S2spZs&name=supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16052)] |\n| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets | GMU | NeurIPS | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=M4h1UAxI3b)] |\n| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | Wyze Labs | NeurIPS Datasets and Benchmarks | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qynH28Y4xE)] [[SUPP](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qynH28Y4xE&name=supplementary_material)] [[DATASET](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fwyzelabs\u002FRuleRecommendation)] |\n| Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning | Google Research | NeurIPS Datasets and Benchmarks | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EPz1DcdPVE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09619)] [[DATASET](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fdataset_grouper)] |\n| Text-driven Prompt Generation for Vision-Language Models in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8zduZGpzZl)] |\n| HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dsWg7n6zoo)] |\n| Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=H0inHCV05c)] |\n| FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XJhL1XlefX)] |\n| FedSoL: Bridging Global Alignment and Local Generality in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WYLhRgBAFH)] |\n| One-shot Empirical Privacy Estimation for Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JmrHzzDiyI)] |\n| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5JsO2DClwk)] |\n| SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=06quMTmtRV)] |\n| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=xqvB784PCv)] |\n| Towards Building the FederatedGPT: Federated Instruction Tuning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TaDiklyVps)] |\n| Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ozN92d7CHX)] |\n| LASER: Linear Compression in Wireless Distributed Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PmahoyE89G)] |\n| MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YqqWQP8POe)] |\n| TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SvJx4a75QZ)] |\n| An Empirical Evaluation of Federated Contextual Bandit Algorithms |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qwnOt7FFSD)] |\n| RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FakNykU4PF)] |\n| FDAPT: Federated Domain-adaptive Pre-training for Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ESCL5T3EgV)] |\n| Making Batch Normalization Great in Federated Deep Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iKQC652XIk)] |\n| Correlated Noise Provably Beats Independent Noise for Differentially Private Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AbrnDOw8R9)] |\n| Parameter Averaging Laws for Multitask Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qQ2qXFu05s)] |\n| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HyRwexERAo)] |\n| Beyond Parameter Averaging in Model Aggregation |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sPtEDSVD4K)] |\n| Augmenting Federated Learning with Pretrained Transformers |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ldN6QdyukS)] |\n| Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=le0Emy9SqA)] |\n| DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=s7hquGszME)] |\n| Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gACRiXPGmM)] |\n| FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PuYD0fh5aq)] |\n| Learning Optimizers for Local SGD |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HiPe4SjZMs)] |\n| Exploring User-level Gradient Inversion with a Diffusion Prior |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lcElZPvMFp)] |\n| User Inference Attacks on Large Language Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4uyyLG4KCH)] |\n| FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1ww9tjEQVL)] |\n| Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EmV9sGpZ7q)] |\n| Backdoor Threats from Compromised Foundation Models to Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BrcHuO2BVc)] |\n| MOFL\u002FD: A Federated Multi-objective Learning Framework with Decomposition |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Pj6BPHZy56)] |\n| Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OoEIUohfcp)] |\n| Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LiSj1GRVhL)] |\n| FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4apX9Kcxie)] |\n| Private and Personalized Histogram Estimation in a Federated Setting |  | NeurIPS workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XSfsvBoc8M)] |\n| The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | COLT | 2023 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv195\u002Fzhao23b.html)] |\n| FLASH: Automating federated learning using CASH | Rensselaer Polytechnic Institute | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5L66DZpPSHk)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Falam23a\u002Falam23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=5L66DZpPSHk&name=other_supplementary_material)] |\n| Personalized federated domain adaptation for item-to-item recommendation | AWS AI Labs | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7ypu4_en3Zm)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03191)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Ffan23a\u002Ffan23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=7ypu4_en3Zm&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzfan20\u002FPFGNNPlus)] |\n| Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning | Baidu Research | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Q06wKxnHRv)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.00532)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fkarimi23a\u002Fkarimi23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Q06wKxnHRv&name=other_supplementary_material)] |\n| Federated learning of models pre-trained on different features with consensus graphs | IBM Research | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gSMiXJmMEOf)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fma23b\u002Fma23b-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=gSMiXJmMEOf&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmatenure\u002Ffederated_feature_fusion)] |\n| Fast Heterogeneous Federated Learning with Hybrid Client Selection | NWPU | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JtSlA972EHP)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fsong23b\u002Fsong23b-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=JtSlA972EHP&name=other_supplementary_material)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.05135)] |\n| Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning | Cornell University | UAI | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Gt_GiNkBhu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10880)] [[SUPP](https:\u002F\u002Fproceedings.mlr.press\u002Fv216\u002Fwu23a\u002Fwu23a-supp.pdf)] [[MATERIAL](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Gt_GiNkBhu&name=other_supplementary_material)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwrh14\u002Flearning_to_invert)] |\n| Dynamic Regularized Sharpness Aware Minimization in Federated Learning:  Approaching Global Consistency and Smooth Landscape | The University of Sydney | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vD1R00hROK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.11584)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F24651.pdf)] |\n| Analysis of Error Feedback in Federated  Non-Convex Optimization with Biased Compression: Fast Convergence and  Partial Participation | LinkedIn Ads | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wbs1fKLfOe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14292)] |\n| FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization | Alibaba Group | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=891ytYlYgB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03966)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Fmaster\u002Fbenchmark\u002FFedHPOBench)] |\n| Federated Conformal Predictors for Distributed Uncertainty Quantification | MIT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YVTr9PzIrK)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17564)] [[CODE](https:\u002F\u002Fgithub.com\u002Fclu5\u002Ffederated-conformal)] |\n| Federated Adversarial Learning: A Framework with Convergence Analysis | UBC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=kgvoV2KcTJ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.03635)] |\n| Federated Heavy Hitter Recovery under Linear Sketching | Google Research | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=zN4oRCrlnM)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.13347)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated)] |\n| Doubly Adversarial Federated Bandits | London School of Economics and Political Science | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FjOB0g7iRf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09223)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjialinyi94\u002Fdoubly-stochastic-federataed-bandit)] |\n| Achieving Linear Speedup in Non-IID Federated Bilevel Learning | UC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XFpTtAWNpQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.05412)] |\n| One-Shot Federated Conformal Prediction | Université Paris-Saclay | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SZJGIWe1Ag)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06322)] [[CODE](https:\u002F\u002Fgithub.com\u002FpierreHmbt\u002FFedCP-QQ)] |\n| Federated Online and Bandit Convex Optimization | TTIC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mi7pnouqLa)] |\n| Federated Linear Contextual Bandits with User-level Differential Privacy | The Pennsylvania State University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=b9opfVNw6O)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05275)] |\n| Vertical Federated Graph Neural Network for Recommender System | NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NRnS6CtbaN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.05786)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmaiph123\u002Fverticalgnn)] |\n| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | University at Buffalo | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IYyhNudD9V)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04969)] |\n| Towards Understanding Ensemble Distillation in Federated Learning | KAIST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xx0TH4IKgQ)] |\n| Personalized Subgraph Federated Learning | KAIST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GXHL8ZS1GX)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10206)] [[CODE](https:\u002F\u002Fgithub.com\u002FJinheonBaek\u002FFED-PUB)] |\n| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift | Lagrange Mathematics and Computing Research Center; CMAP | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ytpEqHYSEy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05131)] |\n| Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ICk7GJ1awE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.14618)] |\n| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=JC05k0E2EM)] [[CODE](https:\u002F\u002Fgithub.com\u002FYamingGuo98\u002FFedIIR)] |\n| Personalized Federated Learning under Mixture of Distributions | UCLA | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nmVOTsQGR9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.01068)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzshuai8\u002FFedGMM_ICML2023)] |\n| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cHJ1VuZorx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19229)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedDisco)] |\n| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ht9r3P6Lts)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05891)] [[CODE](https:\u002F\u002Fgithub.com\u002Fharliwu\u002Ffedamd)] |\n| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=y8qAZhWbNs)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10999)] |\n| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Auburn University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6wQKmKiDHw)] |\n| On the Convergence of Federated Averaging with Cyclic Client Participation | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=d8LTNXt97w)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03109)] |\n| Revisiting Weighted Aggregation in Federated Learning with Neural Networks | ZJU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FuDAjnWhrQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10911)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzexilee\u002Ficml-2023-fedlaw)] |\n| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond | CMU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WfI3I8OjHS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10697)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F24679_ljO6pDE.pdf)] |\n| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning | UESTC; NTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iASUTBGw07)] |\n| Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=q5RHsg6VRw)] |\n| DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VxKr51JjWC)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00543)] |\n| FeDXL: Provable Federated Learning for Deep X-Risk Optimization | Texas A&M University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=C7fNCYdptO)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14396)] [[CODE](https:\u002F\u002Fgithub.com\u002Foptimization-ai\u002Ficml2023_fedxl)] |\n| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | HIT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=AMuNQEUmGr)] [[CODE](https:\u002F\u002Fgithub.com\u002FHypervoyager\u002FPFL)] |\n| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=33fj5Ph3ot)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FpFedGraph)] |\n| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rnNBSMOWvA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06508)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbaowenxuan\u002Ffedcollab)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F23569.pdf)] |\n| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zurich | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mRiDy4qGwB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01785)] [[CODE](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Ftableak)] |\n| FedCR:  Personalized Federated Learning Based on Across-Client Common  Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=YDC5jTS3LR)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaozzh\u002FFedCR)] |\n| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Duke University | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=NcbY2UOfko)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.15245)] |\n| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta AI | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Otdp5SGQMr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.03942)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fdp_compression)] |\n| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning | Owkin Inc. | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pRsJIVcjxD)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.07644)] [[CODE](https:\u002F\u002Fgithub.com\u002Fowkin\u002Fsratta)] |\n| Improving the Model Consistency of Decentralized Federated Learning | THU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fn2NFlYLBL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04083)] |\n| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Alibaba Group | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ieSN7Xyo8g)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.02776)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyxdyc\u002Fpfedgate)] |\n| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=CBLDv6SFMn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12559)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftotilas\u002Fpadadmm)] |\n| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2CiaH2Tq4G)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchaoyitud\u002FLeadFL)] |\n| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HtHFnHrZXu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12961)] [[CODE](https:\u002F\u002Fgithub.com\u002Fybdai7\u002Fchameleon-durable-backdoor)] |\n| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7aqVcrXjxa)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13407)] |\n| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nDKoVwNjMH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13462)] [[CODE](https:\u002F\u002Fgithub.com\u002Flins-lab\u002Ffedbr)] |\n| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=gHfybro5Sj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.00771)] [[SLIDES](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2023\u002FSlides\u002F25109.pdf)] |\n| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta AI | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ai1TyAjZt9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.05578)] |\n| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Leuven | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kz0IODB2kj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.00127)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjunyizhu-ai\u002Fsurrogate_model_extension)] |\n| Fair yet Asymptotically Equal Collaborative Learning | NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5VhltFPSO8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05764)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxqlin98\u002FFair-yet-Equal-CML)] |\n| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uIzkbJgyqc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.08371)] |\n| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DVF7gEQQf7)] |\n| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uGORNDmIdr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06090)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbingzhaozhu\u002Fxtab)] |\n| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=a0kGwNUwil)] |\n| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3DI6Kmw81p)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06079)] [[CODE](https:\u002F\u002Fgithub.com\u002FYuchenLiu-a\u002Fbyzantine-gas)] |\n| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Rensselaer Polytechnic Institute | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=L8iWCxzwl1)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.02219)] |\n| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eqTWOzheZT)] |\n| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | University of Chicago | ICML | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=iAgQfF3atY)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02543)] [[CODE](https:\u002F\u002Fgithub.com\u002Fboxinz17\u002Fdata-market-via-adaptive-sampling)] |\n| Ensemble and continual federated learning for classification tasks. | Universidade de Santiago de Compostela | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06330-z)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07129)] |\n| FAC-fed: Federated adaptation for fairness and concept drift aware stream classification | Leibniz University of Hannover | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-023-06360-7)] |\n| Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06292-8)] |\n| FedLab: A Flexible Federated Learning Framework :fire: | UESTC; Peng Cheng Lab | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0440.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.11621)] [[CODE](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedLab)] |\n| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? |  | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-0224.html)] |\n| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-1301.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04911)] [[CODE](https:\u002F\u002Fgithub.com\u002Fbokun-wang\u002Fmoml)] |\n| A First Look into the Carbon Footprint of Federated Learning | University of Cambridge | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F21-0445.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07627)] |\n| Attacks against Federated Learning Defense Systems and their Mitigation | The University of Newcastle | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0014.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcodymlewis\u002Fviceroy)] |\n| A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates | Universit ́e Cˆ ote d’Azur | JMLR | 2023 | [[PUB](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv24\u002F22-0689.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10189)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002FLabs-Federated-Learning\u002Ftree\u002Fasynchronous_FL)] |\n| Tighter Regret Analysis and Optimization of Online Federated Learning | Hanyang University | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10255290)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.06491)] |\n| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.00522)] |\n| Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10040221)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.10607)] [[CODE](https:\u002F\u002Fgithub.com\u002FShenglongZhou\u002FFedADMM)] |\n| FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9847383)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.13236)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpurp1eHaze\u002FFedIPR)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F562837170)] |\n| Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9850408)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.11375)] |\n| Personalized Federated Learning with Feature Alignment and Classifier Collaboration | THU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SXZr8aDKia)] [[CODE](https:\u002F\u002Fgithub.com\u002FJianXu95\u002FFedPAC)] |\n| MocoSFL: enabling cross-client collaborative self-supervised learning | ASU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2QGJXyMNoPz)] [[CODE](https:\u002F\u002Fgithub.com\u002FSonyAI\u002FMocoSFL)] |\n| Single-shot General Hyper-parameter Optimization for Federated Learning | IBM | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3RhuF8foyPW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08338)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3RhuF8foyPW&name=SUPP_material)] |\n| Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated | Facebook | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Mpa3tRJFBb)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.15387)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwhere_to_begin)] |\n| FedExP: Speeding up Federated Averaging via Extrapolation | CMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=IPrzNbddXV)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09604)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdivyansh03\u002Ffedexp)] |\n| Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection | MSU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mMNimwRb7Gr)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FFOSTER)] |\n| DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity | KAUST | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VA1YpcNr7ul)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.01268)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmysteryresearcher\u002Fdasha)] |\n| Machine Unlearning of Federated Clusters | University of Illinois | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VzwfoFyYDga)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.16424)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=VzwfoFyYDga&name=SUPP_material)] |\n| Federated Neural Bandits | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=38m4h8HcNRL)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14309)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=38m4h8HcNRL&name=SUPP_material)] |\n| FedFA:  Federated Feature Augmentation | ETH Zurich | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=U9yFP90jU0)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12995)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftfzhou\u002Ffedfa)] |\n| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach | CMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dZrQR7OR11)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04228)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhanguo97\u002Fexpectation-propagation)] |\n| Better Generative Replay for Continual Federated Learning | University of Virginia | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cRxYWKiTan)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaiqing98\u002FFedCIL)] |\n| Federated Learning from Small Datasets | IKIM | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hDDV1lsRV8)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03469)] |\n| Federated Nearest Neighbor Machine Translation | USTC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=R1U5G2spbLd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12211)] |\n| Meta Knowledge Condensation for Federated Learning | A*STAR | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TDf-XFAwc79)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.14851)] |\n| Test-Time Robust Personalization for Federated Learning | EPFL | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=3aBuJEza5sq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.10920)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=3aBuJEza5sq&name=SUPP_material)] |\n| DepthFL : Depthwise Federated Learning for Heterogeneous Clients | SNU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pf8RIZTMU58)] |\n| Towards Addressing Label Skews in One-Shot Federated Learning | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rzrqh85f4Sc)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=rzrqh85f4Sc&name=SUPP_material)] |\n| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | NUS | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EXnIyMVTL8s)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00226)] [[CODE](https:\u002F\u002Fgithub.com\u002FYujun-Shi\u002FFedCLS)] |\n| Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=A9WQaxYsfx)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=A9WQaxYsfx&name=SUPP_material)] |\n| SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jh1nCir1R3d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14026)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=jh1nCir1R3d&name=SUPP_material)] |\n| Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses | USC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TVY6GoURrw)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09779)] [[CODE](https:\u002F\u002Fgithub.com\u002Flowya\u002Fprivate-federated-learning-without-a-trusted-server)] |\n| Effective passive membership inference attacks in federated learning against overparameterized models | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QsCSLPP55Ku)] |\n| FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification | University of Cambridge | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=9aokcgBVIj1)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08671)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=9aokcgBVIj1&name=SUPP_material)] |\n| Multimodal Federated Learning via Contrastive Representation Ensemble | THU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Hnk1WRMAYqg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.08888)] [[CODE](https:\u002F\u002Fgithub.com\u002Fflair-thu\u002Fcreamfl)] |\n| Faster federated optimization under second-order similarity | Princeton University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ElC6LYO4MfD)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02257)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=ElC6LYO4MfD&name=SUPP_material)] |\n| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | University of Sydney | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=bZjxxYURKT)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=bZjxxYURKT&name=SUPP_material)] |\n| The Best of Both Worlds: Accurate  Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | utexas | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=29V3AWjVAFi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.08968)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=29V3AWjVAFi&name=SUPP_material)] |\n| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | UBC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hxEIgUXLFF)] [[CODE](https:\u002F\u002Fgithub.com\u002FMehdiSet\u002FPerFedMask)] |\n| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data | GMU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ytZIYmztET)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002Fepisode)] |\n| FedDAR: Federated Domain-Aware Representation Learning | Harvard | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6P9Y25Pljl6)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04007)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlz0414\u002FFedDAR)] |\n| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning | upenn | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oJpVVGXu9i)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FCENTAUR-Privacy-Federated-Representation-Learning)] |\n| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xo2E217_M4n)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.12873)] [[CODE](https:\u002F\u002Fgithub.com\u002FKaiyuanZh\u002FFLIP)] |\n| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses | RUC | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=-EHqoysUYLx)] |\n| Efficient Federated Domain Translation | Purdue University | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=uhLAcrAZ9cJ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=uhLAcrAZ9cJ&name=SUPP_material)] |\n| On the Importance and Applicability of Pre-Training for Federated Learning | OSU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fWWFv--P0xP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.11488)] [[CODE](https:\u002F\u002Fgithub.com\u002Fandytu28\u002Ffps_pre-training)] |\n| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | UMD | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=r0BrY4BiEXO)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12675)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy | UCLA | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FUiDMCr_W4o)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.01771)] |\n| Instance-wise Batch Label Restoration via Gradients in Federated Learning | BUAA | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FIrQfNSOoTr)] [[CODE](https:\u002F\u002Fgithub.com\u002FBUAA-CST\u002FiLRG)] |\n| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity | College of William and Mary | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_hb4vM3jspB)] |\n| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning | University of Warwick | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Kf7Yyf4O0u)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02912)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fcanife)] |\n| Sparse Random Networks for Communication-Efficient Federated Learning | Stanford | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k1FHgri5y3-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.15328)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=k1FHgri5y3-&name=SUPP_material)] |\n| Combating Exacerbated Heterogeneity for Robust Decentralized Models | HKBU | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eKllxpLOOm)] [[CODE](https:\u002F\u002Fgithub.com\u002FZFancy\u002FSFAT)] |\n| Hyperparameter Optimization through Neural Network Partitioning | University of Cambridge | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nAgdXgfmqj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.14766)] |\n| Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? | MIT | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2L9gzS80tA4)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10947)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=2L9gzS80tA4&name=SUPP_material)] |\n| Variance Reduction is an Antidote  to Byzantines: Better Rates, Weaker Assumptions and Communication  Compression as a Cherry on the Top | mbzuai | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pfuqQQCB34)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00529)] [[CODE](https:\u002F\u002Fgithub.com\u002FSamuelHorvath\u002FVR_Byzantine)] |\n| Dual Diffusion Implicit Bridges for Image-to-Image Translation | Stanford | ICLR | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5HLoTvVGDe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08382)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=5HLoTvVGDe&name=SUPP_material)] |\n| An accurate, scalable and verifiable protocol for federated differentially private averaging | INRIA Lille | Mach Learn | 2022 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10994-022-06267-9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07218)] |\n| Federated online clustering of bandits. | CUHK | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rKUgiU8iqeq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.14865)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhaohaoru\u002Ffederated-clustering-of-bandits)] |\n| Privacy-aware compression for federated data analysis. | Meta AI | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BqUdRP8i9e9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08134)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fdp_compression)] |\n| Faster non-convex federated learning via global and local momentum. | UTEXAS | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SSlLRUIs9e9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.04061)] |\n| Fedvarp: Tackling the variance due to partial client participation in federated learning. | CMU | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HlWLLdUocx5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.14130)] |\n| SASH: Efficient secure aggregation based on SHPRG for federated learning | CAS; CASTEST | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HSleBPIoql9)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.12321)] |\n| Bayesian federated estimation of causal effects from observational data | NUS | UAI | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BEl3vP8sqlc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.00456)] |\n| Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Hanyang University | TPAMI | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9625795)] |\n| Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning | ZJU | TPAMI | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9238427)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsunjunaimer\u002FTPAMI-LAQ)] |\n| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox | Moscow Institute of Physics and Technology | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W72rB0wwLVu)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03957)] |\n| LAMP: Extracting Text from Gradients with Language Model Priors | ETHZ | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6iqd9JAVR1z)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=6iqd9JAVR1z&name=SUPP_material)] |\n| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning | utexas | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=G3fswMh9P8y)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13692)] |\n| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond | NUIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_33ynl9VgCX)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05187)] |\n| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | WISC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=i9XrHJoyLqJ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=i9XrHJoyLqJ&name=SUPP_material)] |\n| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | Columbia University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Vj-jYs47cx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10870)] |\n| Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective | PKU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wo-a8Ji6s3A)] |\n| Subspace Recovery from Heterogeneous Data with Non-isotropic Noise | Stanford | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mUeMOdJ2IJp)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.13497)] |\n| EF-BV:  A Unified Theory of Error Feedback and Variance Reduction Mechanisms  for Biased and Unbiased Compression in Distributed Optimization | KAUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=PeJO709WUup)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.04180)] |\n| On-Demand Sampling: Learning Optimally from Multiple Distributions | UC Berkeley | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=FR289LMkmxZ)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=FR289LMkmxZ&name=SUPP_material)] |\n| Improved Utility Analysis of Private CountSketch | ITU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=XFCirHGr4Cs)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.08397)] [[CODE](https:\u002F\u002Fgithub.com\u002Frasmus-pagh\u002Fprivate-countsketch)] |\n| Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | HUAWEI | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=APXedc0hgdT)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=APXedc0hgdT&name=SUPP_material)] |\n| Decentralized Local Stochastic Extra-Gradient for Variational Inequalities | phystech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Y4vT7m4e3d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08315)] |\n| BEER: Fast O(1\u002FT) Rate for Decentralized Nonconvex Optimization with Communication Compression | Princeton | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I47eFCKa1f3)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.13320)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliboyue\u002Fbeer)] |\n| Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning | The University of Tokyo | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KOHC_CYEIuP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.06083)] |\n| Near-Optimal Collaborative Learning in Bandits | INRIA; Inserm | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2xfJ26BuFP)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00121)] [[CODE](https:\u002F\u002Fgithub.com\u002Fclreda\u002Fnear-optimal-federated)] |\n| Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | phystech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=J0nhRuMkdGf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03313)] |\n| Towards Optimal Communication Complexity in Distributed Non-Convex Optimization | TTIC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SNElc7QmMDe)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=SNElc7QmMDe&name=SUPP_material)] |\n| FedPop: A Bayesian Approach for Personalised Federated Learning | Skoltech | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KETwimTQexH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.03611)] |\n| Fairness in Federated Learning via Core-Stability | UIUC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lKULHf7oFDo)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=lKULHf7oFDo&name=SUPP_material)] |\n| SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning | Sorbonne Université | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=25XIE30VHZE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01639)] |\n| FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction | MSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=OtxyysUdBE)] [[CODE](https:\u002F\u002Fgithub.com\u002FMSU-MLSys-Lab\u002FFedRolex)] |\n| On Sample Optimality in Personalized Collaborative and Federated Learning | INRIA | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7EP90NMAoK)] |\n| DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing | HKUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=hPkGV4BPsmv)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02680)] |\n| FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | THU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=5vVSA_cdRqe)] |\n| Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning | KAUST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=edkno3SvKo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.04338)] |\n| VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? | WHU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=vNrSXIFJ9wz)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=edkno3SvKo&name=SUPP_material)] |\n| DENSE: Data-Free One-Shot Federated Learning | ZJU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QFQoxCFYEkA)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12371)] |\n| CalFAT: Calibrated Federated Adversarial Training with Label Skewness | ZJU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8N1NDRGQSQ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14926)] |\n| SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning | OSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wTp4KgVIJ5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00611)] |\n| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=8SilFGuXgmk)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.00690)] |\n| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness | PKU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wFymjzZEEkH)] |\n| Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=sj9l1JCrAk6)] |\n| BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | UC Berkeley | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jzd2bE5MxW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.06343)] |\n| Byzantine-tolerant federated Gaussian process regression for streaming data | PSU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Nx4gNemvNvx)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Nx4gNemvNvx&name=SUPP_material)] |\n| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | CMU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=tz1PRT6lfLe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.09888)] |\n| Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering | Yale | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=N0tKCpMhA2)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14664)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaoyuzhao123\u002Fcoreset-vfl-codes)] |\n| Communication Efficient Federated Learning for Generalized Linear Bandits | University of Virginia | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xwz9B6LDM5c)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Xwz9B6LDM5c&name=SUPP_material)] |\n| Recovering Private Text in Federated Learning of Language Models | Princeton | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dqgzfhHd2-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.08514)] [[CODE](https:\u002F\u002Fgithub.com\u002FPrinceton-SysML\u002FFILM)] |\n| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach | UTS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mhQLcMjWw75)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.10083)] |\n| Global Convergence of Federated Learning for Mixed Regression | Northeastern University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DdxNka9tMRd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07279)] |\n| Resource-Adaptive Federated Learning with All-In-One Neural Composition | JHU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wfel7CjOYk)] |\n| Self-Aware Personalized Federated Learning | Amazon | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=EqJ5_hZSqgy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.08069)] |\n| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Northeastern University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TATzsweWfof)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.01132)] |\n| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fJt2KFnRqZ)] |\n| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning | EPFL | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4_oCZgBIVI)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08307)] |\n| Personalized Online Federated Multi-Kernel Learning | UCI | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=wUctlvhsNWg)] |\n| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | Duke University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1GAjC_FauE)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.01432)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=1GAjC_FauE&name=SUPP_material)] |\n| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qSs7C7c4G8D)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.13648)] |\n| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=qw3MZb1Juo)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.03097)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=qw3MZb1Juo&name=SUPP_material)] |\n| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | University of Oxford | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=mrt90D00aQX)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=mrt90D00aQX&name=SUPP_material)] |\n| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching | KAIST | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ql75oqz1npy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00270)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=Ql75oqz1npy&name=SUPP_material)] |\n| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits | UC | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Fx7oXUVEPW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.03106)] |\n| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Tulane University | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=4OHRr7gmhd4)] |\n| On Privacy and Personalization in Cross-Silo Federated Learning | CMU | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Oq2bdIQQOIZ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07902)] |\n| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | NUS | NeurIPS | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fiBnhdazkyx)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06312)] [[CODE](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FFedSim)] |\n| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | Owkin | NeurIPS Datasets and Benchmarks | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GgM5DiAb6A2)] [[CODE](https:\u002F\u002Fgithub.com\u002Fowkin\u002FFLamby)] |\n| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources | University of Pittsburgh | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftan22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06261)] [[CODE](https:\u002F\u002Fgithub.com\u002Fellenxtan\u002Fifedtree)] |\n| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fbao22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.08204)] [[CODE](https:\u002F\u002Fgithub.com\u002FMingruiLiu-ML-Lab\u002FFederated-Sparse-Learning)] |\n| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | NYU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fbietti22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.05318)] [[CODE](https:\u002F\u002Fgithub.com\u002Falbietz\u002Fppsgd)] |\n| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning :fire: | Stanford; Google Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fchen22c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03761)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fprivate_linear_compression)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17529.pdf)] |\n| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Google Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fchen22s.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09916)] [[CODE](https:\u002F\u002Fgithub.com\u002FWeiNingChen\u002Fpbm)] |\n| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training | USTC | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fdai22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00187)] [[CODE](https:\u002F\u002Fgithub.com\u002Frong-dai\u002FDisPFL)] |\n| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning | University of Oulu | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Felgabli22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.08829)] [[CODE](https:\u002F\u002Fgithub.com\u002Faelgabli\u002FFedNew)] |\n| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning | University of Cambridge | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fhonig22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.00465)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16009.pdf)] [[CODE](https:\u002F\u002Fmedia.icml.cc\u002FConferences\u002FICML2022\u002FSUPP\u002Fhonig22a-supp.zip)] |\n| Accelerated Federated Learning with Decoupled Adaptive Optimization | Auburn University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fjin22e.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.07223)] |\n| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Georgia Tech | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fkhodadadian22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10185)] |\n| Multi-Level Branched Regularization for Federated Learning | Seoul National University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fkim22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.06936)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjinkyu032\u002FFedMLB)] [[PAGE](http:\u002F\u002Fcvlab.snu.ac.kr\u002Fresearch\u002FFedMLB\u002F)] |\n| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale :fire: | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flai22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.11367)] [[CODE](https:\u002F\u002Fgithub.com\u002FSymbioticLab\u002FFedScale)] |\n| Federated Learning with Positive and Unlabeled Data | XJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flin22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10904)] [[CODE](https:\u002F\u002Fgithub.com\u002Flittlesunlxy\u002Ffedpu-torch)] |\n| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fliu22k.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002FGAMF)] |\n| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flubana22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.11506)] [[CODE](https:\u002F\u002Fgithub.com\u002Fakhilmathurs\u002Forchestra)] |\n| Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring | USTC | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fluo22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.06818)] [[CODE](https:\u002F\u002Fgithub.com\u002Fluozhengquan\u002FDFL)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16881.pdf)] [[解读](https:\u002F\u002Fwww.bilibili.com\u002Fread\u002Fcv17092678)] |\n| Architecture Agnostic Federated Learning for Neural Networks | The University of Texas at Austin | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmakhija22a.html)] [[PDF](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16926.pdf)] |\n| Personalized Federated Learning through Local Memorization | Inria | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmarfoq22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.09360)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fknn-per)] |\n| Proximal and Federated Random Reshuffling | KAUST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fmishchenko22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.06704)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkonstmish\u002Frr_prox_fed)] |\n| Federated Learning with Partial Model Personalization | University of Washington | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fpillutla22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03809)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkrishnap25\u002FFL_partial_personalization)] |\n| Generalized Federated Learning via Sharpness Aware Minimization | University of South Florida | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fqu22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02618)] |\n| FedNL: Making Newton-Type Methods Applicable to Federated Learning | KAUST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fsafaryan22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02969)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_VYCEWT17R0&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17084.pdf)] |\n| Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | CMU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fsharma22c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.04850)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17435.pdf)] |\n| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning | Hong Kong Baptist University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftang22d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02465)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwizard1203\u002FVHL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F548508633)] |\n| FedNest: Federated Bilevel, Minimax, and Compositional Optimization | University of Michigan | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftarzanagh22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.02215)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmc-nya\u002FFedNest)] |\n| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | VMware Research | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fvargaftik22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.08842)] [[CODE](https:\u002F\u002Fgithub.com\u002Famitport\u002FEDEN-Distributed-Mean-Estimation)] |\n| Communication-Efficient Adaptive Federated Learning | Pennsylvania State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22o.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.02719)] |\n| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | CISPA Helmholz Center for Information Security | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22y.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.05323)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F16194_hmjFNsN.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fa514514772\u002FProgFed)] |\n| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification :fire: | University of Maryland | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwen22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00580)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| Anarchic Federated Learning | The Ohio State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyang22r.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.09875)] |\n| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyi22a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLipingYi\u002FQSFL)] |\n| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyoon22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.11453)] |\n| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyue22a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.03681)] [[CODE](https:\u002F\u002Fgithub.com\u002FKAI-YUE\u002Fntk-fed)] |\n| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13673)] |\n| Personalized Federated Learning via Variational Bayesian Inference | CAS | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22o.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07977)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002Ficml-2022\u002FSlides\u002F17302.pdf)] [[UC.](https:\u002F\u002Fgithub.com\u002FAllenBeau\u002FpFedBayes)] |\n| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] |\n| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22w.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10341)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjhcknzzm\u002FFederated-Learning-Backdoor\u002F)] |\n| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | Michigan State University | ICML | 2022 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhu22e.html)] |\n| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST | ICLR (oral) | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LdlwbBP2mlq)] [[CODE](https:\u002F\u002Fopenreview.net\u002Fattachment?id=LdlwbBP2mlq&name=SUPP_material)] |\n| Bayesian Framework for Gradient Leakage | ETH Zurich | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=f2lrIbGx3x7)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.04706)] [[CODE](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Fbayes-framework-leakage)] |\n| Federated Learning from only unlabeled data with class-conditional-sharing clients | The University of Tokyo; CUHK | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=WHA8009laxu)] [[CODE](https:\u002F\u002Fgithub.com\u002Flunanbit\u002FFedUL)] |\n| FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning | CMU; University of Illinois at Urbana-Champaign; University of Washington | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZaVVVlcdaN)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06869.)] |\n| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | THU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=541PxiEKN3F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.02645)] [[CODE](https:\u002F\u002Fgithub.com\u002FZoesgithub\u002FFedReg)] |\n| FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning | POSTECH | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=d71n4ftoCBy)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06098)] [[CODE](https:\u002F\u002Fgithub.com\u002FSouth-hw\u002FFedPara_ICLR22)] |\n| An Agnostic Approach to Federated Learning with Class Imbalance | University of Pennsylvania | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Xo0lbDt975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshenzebang\u002FFederated-Learning-Pytorch)] |\n| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; The University of Texas at Austin | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_QLmakITKg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09747)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FSplitMix)] |\n| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models :fire: | University of Maryland; NYU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=fwzUgo0FM9v)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.13057)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Fbreaching)] |\n| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; University of Oxford | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2sDQwC_hmnM)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.02507)] |\n| Diverse Client Selection for Federated Learning via Submodular Maximization | Intel; CMU | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nwKXyFvaUm)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmelodi-lab\u002Fdivfl)] |\n| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | Purdue | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B7ZbqNLDn-_)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.00280)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshams-sam\u002FFedOptim)] |\n| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions :fire: | University of Maryland; Google | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=E4EE_ohFGz)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002F7525c36324cb022bc05c3fce88ef01147cae9740\u002Fperiodic_distribution_shift)] |\n| Towards Model Agnostic Federated Learning Using Knowledge Distillation | EPFL | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=lQI_mZjvBxj)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.15210)] [[CODE](https:\u002F\u002Fgithub.com\u002FAfoninAndrei\u002FICLR2022)] |\n| Divergence-aware Federated Self-Supervised Learning | NTU; SenseTime | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=oVE1z8NlNe)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04385)] [[CODE](https:\u002F\u002Fgithub.com\u002FEasyFL-AI\u002FEasyFL)] |\n| What Do We Mean by Generalization in Federated Learning? :fire: | Stanford; Google | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=VimqQq-i_Q)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14216)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fgeneralization)] |\n| FedBABU: Toward Enhanced Representation for Federated Image Classification | KAIST | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HuaYQfggn5u)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06042)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjhoon-oh\u002FFedBABU)] |\n| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | EPFL | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jXKKDEi5vJt)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09365)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliehe\u002Fbyzantine-robust-noniid-optimizer)] |\n| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | Aibee | ICLR Spotlight | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=7l1IjZVddDW)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.12467)] [[PAGE](https:\u002F\u002Firvingmeng.github.io\u002Fprojects\u002Fprivacyface\u002F)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F484920301)] |\n| Hybrid Local SGD for Federated Learning with Heterogeneous Communications | University of Texas; Pennsylvania State University | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=H0oaWl6THa)] |\n| On Bridging Generic and Personalized Federated Learning for Image Classification | The Ohio State University | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=I1hQbx10Kxn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.00778)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongyouc\u002FFed-RoD)] |\n| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LdlwbBP2mlq)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10342)] |\n| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. |  | JMLR | 2021 | [[PUB](http:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv22\u002F19-1048.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsabersalehk\u002FMRE_C)] |\n| Constrained differentially private federated learning for low-bandwidth devices |  | UAI | 2021 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv161\u002Fkerkouche21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00342)] |\n| Federated stochastic gradient Langevin dynamics |  | UAI | 2021 | [[PUB](https:\u002F\u002Fproceedings.mlr.press\u002Fv161\u002Fmekkaoui21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11231)] |\n| Federated Learning Based on Dynamic Regularization | BU; ARM | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=B7v4QMR6Z9w)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.04263)] [[CODE](https:\u002F\u002Fgithub.com\u002FAntixK\u002FFedDyn)] |\n| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | The Ohio State University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jDdzh5ul-d)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.11203)] |\n| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Duke University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=TNkPBBYFkXg)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.01264)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdem123456789\u002FHeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)] |\n| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=Ogga20D2HO-)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.00233)] |\n| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms :fire: | CMU; Google | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=GFsU8a0sGB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.05273)] [[CODE](https:\u002F\u002Fgithub.com\u002Falshedivat\u002Ffedpa)] |\n| Adaptive Federated Optimization :fire: | Google | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=LkFG3lB13U5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.00295)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Foptimization)] |\n| Personalized Federated Learning with First Order Model Optimization | Stanford; NVIDIA | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ehJqJQk9cw)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.08565)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FFedFomo)] [[UC.](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FPFL-Non-IID)] |\n| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization :fire: | Princeton | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=6YEQUn0QICG)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07623)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmed-air\u002FFedBN)] |\n| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | The Ohio State University | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=dgtpE6gKjHn)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.01974)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongyouc\u002Ffedbe)] |\n| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ce6CFXBh30h)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12097)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwyjeong\u002FFedMatch)] |\n| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Ffeng21f.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.09757)] [[CODE](https:\u002F\u002Fgithub.com\u002FFengHZ\u002FKD3A)] [[解读](https:\u002F\u002Fmp.weixin.qq.com\u002Fs\u002FgItgiZmKUxg0ltaeOVdnRw)] |\n| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Harvard University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Flam21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06089)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38958558\u002Fgradient-disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgdisag\u002Fgradient_disaggregation)] |\n| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fhuang21c.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05001)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959650\u002Fflntk-a-neural-tangent-kernelbased-framework-for-federated-learning-analysis)] |\n| Personalized Federated Learning using Hypernetworks :fire: | Bar-Ilan University; NVIDIA | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fshamsian21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04628)] [[CODE](https:\u002F\u002Fgithub.com\u002FAvivSham\u002FpFedHN)] [[PAGE](https:\u002F\u002Favivsham.github.io\u002Fpfedhn\u002F)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959583\u002Fpersonalized-federated-learning-using-hypernetworks)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F431130945)] |\n| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyuan21d.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.08474)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002FFCO-ICML21)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https:\u002F\u002Fhongliny.github.io\u002Ffiles\u002FFCO_ICML21\u002FFCO_ICML21_slides.pdf)] |\n| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; University of Pennsylvania | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fcollins21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07078)] [[CODE](https:\u002F\u002Fgithub.com\u002Flgcollins\u002FFedRep)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959519\u002Fexploiting-shared-representations-for-personalized-federated-learning)] |\n| Data-Free Knowledge Distillation for Heterogeneous Federated Learning :fire: | Michigan State University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fzhu21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.10056)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhuangdizhu\u002FFedGen)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959429\u002Fdatafree-knowledge-distillation-for-heterogeneous-federated-learning)] |\n| Federated Continual Learning with Weighted Inter-client Transfer | KAIST | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyoon21b.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03196)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwyjeong\u002FFedWeIT)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959323\u002Ffederated-continual-learning-with-weighted-interclient-transfer)] |\n| Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity | The University of Iowa | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fyuan21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.04635)] [[CODE](https:\u002F\u002Flibauc.org\u002F)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959235\u002Ffederated-deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity)] |\n| Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning | The University of Tokyo | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fmurata21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03198)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959169\u002Fbiasvariance-reduced-local-sgd-for-less-heterogeneous-federated-learning)] |\n| Federated Learning of User Verification Models Without Sharing Embeddings | Qualcomm | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fhosseini21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.08776)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38958858\u002Ffederated-learning-of-user-verification-models-without-sharing-embeddings)] |\n| Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | Accenture | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Ffraboni21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.05883)] [[CODE](https:\u002F\u002Fgithub.com\u002FAccenture\u002F\u002FLabs-Federated-Learning\u002Ftree\u002Fclustered_sampling)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959618\u002Fclustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning)] |\n| Ditto: Fair and Robust Federated Learning Through Personalization | CMU; Facebook AI | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fli21h.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.04221)] [[CODE](https:\u002F\u002Fgithub.com\u002Flitian96\u002Fditto)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38955195\u002Fditto-fair-and-robust-federated-learning-through-personalization)] |\n| Heterogeneity for the Win: One-Shot Federated Clustering | CMU | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fdennis21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00697)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959380\u002Fheterogeneity-for-the-win-oneshot-federated-clustering)] |\n| The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation :fire: | Google | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fkairouz21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.06387)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fdistributed_dp)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959306\u002Fthe-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation)] |\n| Debiasing Model Updates for Improving Personalized Federated Training | BU; Arm | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Facar21a.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvenkatesh-saligrama\u002FPersonalized-Federated-Learning)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959212\u002Fdebiasing-model-updates-for-improving-personalized-federated-training)] |\n| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Cornell University | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fblum21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03228)] [[CODE](https:\u002F\u002Fgithub.com\u002Frlphilli\u002FCollaborative-Incentives)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959135\u002Fone-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] |\n| CRFL: Certifiably Robust Federated Learning against Backdoor Attacks | UIUC; IBM | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Fxie21a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08283)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FCRFL)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959047\u002Fcrfl-certifiably-robust-federated-learning-against-backdoor-attacks)] |\n| Federated Learning under Arbitrary Communication Patterns | Indiana University; Amazon | ICML | 2021 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv139\u002Favdiukhin21a.html)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38959048\u002Ffederated-learning-under-arbitrary-communication-patterns)] |\n| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=eNB4WXnNczJ)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.09461)] |\n| Boosting with Multiple Sources | Google | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=1oP1duoZxx)] |\n| DRIVE: One-bit Distributed Mean Estimation | VMware | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=KXRTmcv3dQ8)] [[CODE](https:\u002F\u002Fgithub.com\u002Famitport\u002FDRIVE-One-bit-Distributed-Mean-Estimation)] |\n| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yRfsADObu18)] [[CODE](https:\u002F\u002Fgithub.com\u002FXinyiYS\u002FGradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning)] |\n| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffa84632d742f2729dc32ce8cb5d49733-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14962)] [[CODE](https:\u002F\u002Fgithub.com\u002Fml-postech\u002Fgradient-inversion-generative-image-prior)] |\n| Distributed Machine Learning with Sparse Heterogeneous Data | University of Oxford | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=F9HNBbytcqT)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.01417)] |\n| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | UCLA | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SPrVNsXnGd)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.08763)] |\n| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F076a8133735eb5d7552dc195b125a454-Abstract.html)] |\n| CAFE: Catastrophic Data Leakage in Vertical Federated Learning | Rensselaer Polytechnic Institute; IBM Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F08040837089cdf46631a10aca5258e16-Abstract.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FDeRafael\u002FCAFE)] |\n| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee | NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F080acdcce72c06873a773c4311c2e464-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14074)] [[CODE](https:\u002F\u002Fgithub.com\u002Fflint-xf-fan\u002FByzantine-Federeated-RL)] |\n| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Cornell University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F09a5e2a11bea20817477e0b1dfe2cc21-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09580)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkpdonahue\u002Fmodel_sharing_games)] |\n| QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | UCLA | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F1dba3025b159cd9354da65e2d0436a31-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.13892)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzkhku\u002Ffedsage)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F430789355)] |\n| The Skellam Mechanism for Differentially Private Federated Learning :fire: | Google Research; CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F285baacbdf8fda1de94b19282acd23e2-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04995)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fdistributed_dp)] |\n| No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | NUS; Huawei | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F2f2b265625d76a6704b08093c652fd79-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05001)] |\n| STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | UMN | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F3016a447172f3045b65f5fc83e04b554-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.10435)] |\n| Subgraph Federated Learning with Missing Neighbor Generation | Emory;  UBC; Lehigh University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F34adeb8e3242824038aa65460a47c29e-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13430)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzkhku\u002Ffedsage)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F423555171)] |\n| Evaluating Gradient Inversion Attacks and Defenses in Federated Learning :fire: | Princeton | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F3b3fff6463464959dcd1b68d0320f781-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.00059)] [[CODE](https:\u002F\u002Fgithub.com\u002FPrinceton-SysML\u002FGradAttack)] |\n| Personalized Federated Learning With Gaussian Processes | Bar-Ilan University | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002F46d0671dd4117ea366031f87f3aa0093-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.15482)] [[CODE](https:\u002F\u002Fgithub.com\u002FIdanAchituve\u002FpFedGP)] |\n| Differentially Private Federated Bayesian Optimization with Distributed Exploration | MIT; NUS | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F4c27cea8526af8cfee3be5e183ac9605-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14153)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaizhongxiang\u002FDifferentially-Private-Federated-Bayesian-Optimization)] |\n| Parameterized Knowledge Transfer for Personalized Federated Learning | PolyU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F5383c7318a3158b9bc261d0b6996f7c2-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.02862)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcugzj\u002FKT-pFL)] |\n| Federated Reconstruction: Partially Local Federated Learning :fire: | Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03448)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Freconstruction)] [[UC.](https:\u002F\u002Fgithub.com\u002FKarhouTam\u002FFedRecon)] |\n| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | THU; Princeton; MIT | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F64be20f6dd1dd46adf110cf871e3ed35-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04159)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhmgxr128\u002FMIFA_code\u002F)] |\n| FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective | Duke University; Accenture Labs | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F692baebec3bb4b53d7ebc3b9fabac31b-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.13864)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjeremy313\u002FFL-WBC)] |\n| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout | KAUST; Samsung AI Center | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F6aed000af86a084f9cb0264161e29dd3-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.13451)] |\n| Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients | University of Pennsylvania | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F7a6bda9ad6ffdac035c752743b7e9d0e-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07053)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F35898)] |\n| Federated Multi-Task Learning under a Mixture of Distributions | INRIA; Accenture Labs | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F82599a4ec94aca066873c99b4c741ed8-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.10252)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002FFedEM)] |\n| Federated Graph Classification over Non-IID Graphs | Emory | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002F9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.13423)] [[CODE](https:\u002F\u002Fgithub.com\u002FOxfordblue7\u002FGCFL)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F430718887)] |\n| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing | CMU; Hewlett Packard Enterprise | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fa0205b87490c847182672e8d371e9948-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04502)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmkhodak\u002Ffedex)] |\n| On Large-Cohort Training for Federated Learning :fire: | Google; CMU | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fab9ebd57177b5106ad7879f0896685d4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07820)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Ff4e26c1b9b47ac320e520a8b9943ea2c5324b8c2\u002Flarge_cohort)] |\n| DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | KAUST; Columbia University; University of Central Florida | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fb0ab42fcb7133122b38521d13da7120b-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03112)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhangxu0304\u002FDeepReduce)] |\n| PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization | Huawei | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fc429429bf1f2af051f2021dc92a8ebea-Abstract.html)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F37327)] |\n| Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fceb0595112db2513b9325a85761b7310-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.01338)] |\n| Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning | THU; Alibaba; Weill Cornell Medicine | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fdb8e1af0cb3aca1ae2d0018624204529-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.08435)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuis15\u002FFCFL)] |\n| Federated Linear Contextual Bandits | The Pennsylvania State University; Facebook; University of Virginia | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Fe347c51419ffb23ca3fd5050202f9c3d-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.14177)] [[CODE](https:\u002F\u002Fgithub.com\u002FRuiquan5514\u002FFederated-Linear-Contextual-Bandits)] |\n| Few-Round Learning for Federated Learning | KAIST | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff065d878ccfb4cc4f4265a4ff8bafa9a-Abstract.html)] |\n| Breaking the centralized barrier for cross-device federated learning | EPFL; Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff0e6be4ce76ccfa73c5a540d992d0756-Abstract.html)] [[CODE](https:\u002F\u002Ffedjax.readthedocs.io\u002Fen\u002Flatest\u002Ffedjax.algorithms.html#module-fedjax.algorithms.mime)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F37564)] |\n| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Google Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ff740c8d9c193f16d8a07d3a8a751d13f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.02083)] |\n| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazon; Google | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffc03d48253286a798f5116ec00e99b2b-Abstract.html)] [[PAGE](https:\u002F\u002Fdga.hanlab.ai\u002F)] [[SLIDE](https:\u002F\u002Fdga.hanlab.ai\u002Fassets\u002Fdga_slides.pdf)] |\n| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization | University of North Carolina at Chapel Hill; IBM Research | NeurIPS | 2021 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Fhash\u002Ffe7ee8fc1959cc7214fa21c4840dff0a-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03452)] [[CODE](https:\u002F\u002Fgithub.com\u002Func-optimization\u002FFedDR)] |\n| Federated Adversarial Domain Adaptation | BU; Columbia University; Rutgers University | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJezF3VYPB)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02054)] [[CODE](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1OekTpqB6qLfjlE2XUjQPm3F110KDMFc0\u002Fview?usp=sharing)] |\n| DBA: Distributed Backdoor Attacks against Federated Learning | ZJU; IBM Research | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rkgyS0VFvr)] [[CODE](https:\u002F\u002Fgithub.com\u002FAI-secure\u002FDBA)] |\n| Fair Resource Allocation in Federated Learning :fire: | CMU; Facebook AI | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ByexElSYDr)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.10497)] [[CODE](https:\u002F\u002Fgithub.com\u002Flitian96\u002Ffair_flearn)] |\n| Federated Learning with Matched Averaging :fire: | University of Wisconsin-Madison; IBM Research | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BkluqlSFDS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06440)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002FFedMA)] |\n| Differentially Private Meta-Learning | CMU | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rJgqMRVYvr)] [[PDF](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fzhang22p.html)] |\n| Generative Models for Effective ML on Private, Decentralized Datasets :fire: | Google | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SJgaRA4FPH)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.06679)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ffederated\u002Ftree\u002Fmaster\u002Fgans)] |\n| On the Convergence of FedAvg on Non-IID Data :fire: | PKU | ICLR | 2020 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=HJxNAnVtDS)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.02189)] [[CODE](https:\u002F\u002Fgithub.com\u002Flx10077\u002Ffedavgpy)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F500005337)] |\n| FedBoost: A Communication-Efficient Algorithm for Federated Learning | Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fhamer20a.html)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928463\u002Ffedboost-a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest)] |\n| FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; Amazon | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Frothchild20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07682)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928454\u002Ffetchsgd-communicationefficient-federated-learning-with-sketching)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkiddyboots216\u002FCommEfficient)] |\n| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fkarimireddy20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.06378)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38927610\u002Fscaffold-stochastic-controlled-averaging-for-federated-learning)] [[UC.](https:\u002F\u002Fgithub.com\u002Framshi236\u002FAccelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F538941775)] |\n| Federated Learning with Only Positive Labels | Google | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fyu20f.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10342)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928322\u002Ffederated-learning-with-only-positive-labels)] |\n| From Local SGD to Local Fixed-Point Methods for Federated Learning | Moscow Institute of Physics and Technology; KAUST | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fmalinovskiy20a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.01442)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002FSlides\u002Ficml\u002F2020\u002Fvirtual)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38928320\u002Ffrom-local-sgd-to-local-fixed-point-methods-for-federated-learning)] |\n| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization | KAUST | ICML | 2020 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fli20g.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11364)] [[SLIDE](https:\u002F\u002Ficml.cc\u002Fmedia\u002FSlides\u002Ficml\u002F2020\u002Fvirtual)] [[VIDEO](https:\u002F\u002Fslideslive.com\u002F38927921\u002Facceleration-for-compressed-gradient-descent-in-distributed-optimization)] |\n| Differentially-Private Federated Linear Bandits | MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2020\u002Fhash\u002F4311359ed4969e8401880e3c1836fbe1-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11425)] [[CODE](https:\u002F\u002Fgithub.com\u002Fabhimanyudubey\u002Fprivate_federated_linear_bandits)] |\n| Federated Principal Component Analysis | University of Cambridge; Quine Technologies | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F47a658229eb2368a99f1d032c8848542-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.08059)] [[CODE](https:\u002F\u002Fgithub.com\u002Fandylamp\u002Ffederated_pca)] |\n| FedSplit: an algorithmic framework for fast federated optimization | UC Berkeley | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F4ebd440d99504722d80de606ea8507da-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.05238)] |\n| Federated Bayesian Optimization via Thompson Sampling | NUS; MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F6dfe08eda761bd321f8a9b239f6f4ec3-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.10154)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdaizhongxiang\u002FFederated_Bayesian_Optimization)] |\n| Lower Bounds and Optimal Algorithms for Personalized Federated Learning | KAUST | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F187acf7982f3c169b3075132380986e4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.02372)] |\n| Robust Federated Learning: The Case of Affine Distribution Shifts | UC Santa Barbara; MIT | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Ff5e536083a438cec5b64a4954abc17f1-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08907)] [[CODE](https:\u002F\u002Fgithub.com\u002Ffarzanfarnia\u002FRobustFL)] |\n| An Efficient Framework for Clustered Federated Learning | UC Berkeley; DeepMind | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe32cc80bf07915058ce90722ee17bb71-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.04088)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjichan3751\u002Fifca)] |\n| Distributionally Robust Federated Averaging :fire: | Pennsylvania State University | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12660)] [[CODE](https:\u002F\u002Fgithub.com\u002FMLOPTPSU\u002FFedTorch)] |\n| Personalized Federated Learning with Moreau Envelopes :fire: | The University of Sydney | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Ff4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08848)] [[CODE](https:\u002F\u002Fgithub.com\u002FCharlieDinh\u002FpFedMe)] |\n| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | MIT; UT Austin | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F24389bfe4fe2eba8bf9aa9203a44cdad-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.07948)] [[UC.](https:\u002F\u002Fgithub.com\u002FKarhouTam\u002FPer-FedAvg)] |\n| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge | USC | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fa1d4c20b182ad7137ab3606f0e3fc8a4-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14513)] [[CODE](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FFedML\u002Ftree\u002Fmaster\u002Ffedml_experiments\u002Fdistributed\u002Ffedgkt)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F536901871)] |\n| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization :fire: | CMU; Princeton | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F564127c03caab942e503ee6f810f54fd-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.07481)] [[CODE](https:\u002F\u002Fgithub.com\u002FJYWa\u002FFedNova)] [[UC.](https:\u002F\u002Fgithub.com\u002Fcarbonati\u002Ffl-zoo)] |\n| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | University of Wisconsin-Madison | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fb8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.05084)] |\n| Federated Accelerated Stochastic Gradient Descent | Stanford | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F39d0a8908fbe6c18039ea8227f827023-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08950)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongliny\u002FFedAc-NeurIPS20)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FK28zpAgg3HM)] |\n| Inverting Gradients - How easy is it to break privacy in federated learning? :fire: | University of Siegen | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fc4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.14053)] [[CODE](https:\u002F\u002Fgithub.com\u002FJonasGeiping\u002Finvertinggradients)] |\n| Ensemble Distillation for Robust Model Fusion in Federated Learning | EPFL | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F18df51b97ccd68128e994804f3eccc87-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07242)] [[CODE](https:\u002F\u002Fgithub.com\u002Fepfml\u002Ffederated-learning-public-code\u002Ftree\u002Fmaster\u002Fcodes\u002FFedDF-code)] |\n| Throughput-Optimal Topology Design for Cross-Silo Federated Learning | INRIA | NeurIPS | 2020 | [[PUB](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe29b722e35040b88678e25a1ec032a21-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.12229)] [[CODE](https:\u002F\u002Fgithub.com\u002Fomarfoq\u002Fcommunication-in-cross-silo-fl)] |\n| Bayesian Nonparametric Federated Learning of Neural Networks :fire: | IBM | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fyurochkin19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12022)] [[CODE](https:\u002F\u002Fgithub.com\u002FIBM\u002Fprobabilistic-federated-neural-matching)] |\n| Analyzing Federated Learning through an Adversarial Lens :fire: | Princeton; IBM | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fbhagoji19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.12470)] [[CODE](https:\u002F\u002Fgithub.com\u002Finspire-group\u002FModelPoisoning)] |\n| Agnostic Federated Learning | Google | ICML | 2019 | [[PUB](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fmohri19a.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00146)] |\n| cpSGD: Communication-efficient and differentially-private distributed SGD | Princeton; Google | NeurIPS | 2018 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2018\u002Fhash\u002F21ce689121e39821d07d04faab328370-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10559)] |\n| Federated Multi-Task Learning :fire: | Stanford; USC; CMU | NeurIPS | 2017 | [[PUB](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017\u002Fhash\u002F6211080fa89981f66b1a0c9d55c61d0f-Abstract.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10467)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgingsmith\u002Ffmtl)] |\n\n\u003C!-- 结束：fl-在顶级数据挖掘会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n\n## 联邦学习在顶级数据挖掘会议和期刊中\n\n被顶级数据挖掘（Data Mining, DM）会议和期刊收录的联邦学习论文，包括 [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fkdd\u002Findex.html)（ACM SIGKDD 知识发现与数据挖掘会议）和 [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fwsdm\u002Findex.html)（Web 搜索与数据挖掘会议）。\n\n- [KDD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AKDD%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3690624), [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3637528), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3580305)([研究赛道](https:\u002F\u002Fkdd.org\u002Fkdd2023\u002Fresearch-track-papers\u002F), [应用数据科学赛道](https:\u002F\u002Fkdd.org\u002Fkdd2023\u002Fads-track-papers\u002F), [研讨会](https:\u002F\u002Ffl4data-mining.github.io\u002Fpapers\u002F)), 2022([研究赛道](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperRT.html), [应用数据科学赛道](https:\u002F\u002Fkdd.org\u002Fkdd2022\u002FpaperADS.html)), [2021](https:\u002F\u002Fkdd.org\u002Fkdd2021\u002Faccepted-papers\u002Findex), [2020](https:\u002F\u002Fwww.kdd.org\u002Fkdd2020\u002Faccepted-papers)\n- [WSDM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWSDM%3A) [2025](https:\u002F\u002Fwww.wsdm-conference.org\u002F2025\u002Faccepted-papers\u002F), [2024](https:\u002F\u002Fwww.wsdm-conference.org\u002F2024\u002Faccepted-papers\u002F), [2023](https:\u002F\u002Fwww.wsdm-conference.org\u002F2023\u002Fprogram\u002Faccepted-papers), [2022](https:\u002F\u002Fwww.wsdm-conference.org\u002F2022\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww.wsdm-conference.org\u002F2021\u002Faccepted-papers.php), [2019](https:\u002F\u002Fwww.wsdm-conference.org\u002F2019\u002Faccepted-papers.php)\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级数据挖掘会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：fl-在顶级数据挖掘会议和期刊中 -->\n\n|Title                                                           |    Affiliation                                                   |    Venue                     |    Year    |    Materials|\n| ------------------------------------------------------------ | ---------------------------------------------------------- | ---------------------- | ---- | ------------------------------------------------------------ |\n| Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709235)] |\n| Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709341)] |\n| Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709243)] |\n| FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709346)] |\n| Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709284)] |\n| PraFFL: A Preference-Aware Scheme in Fair Federated Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709217)] |\n| Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709311)] |\n| BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning |  | KDD | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3690624.3709309)] |\n| Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703513)] |\n| FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703493)] |\n| Towards Personalized Federated Multi-Scenario Multi-Task Recommendation |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703523)] |\n| Density-aware and Cluster-based Federated Anomaly Detection on Data Streams |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3703548)] |\n| Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management |  | WSDM | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3701551.3704125)] |\n| FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics |  | KDD Workshop | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671490)] |\n| Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671722)] |\n| *BadSampler:* Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671879)] |\n| Federated Graph Learning with Structure Proxy Alignment |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671717)] |\n| HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671660)] |\n| FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671545)] |\n| Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671590)] |\n| FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671573)] |\n| On the Convergence of Zeroth-Order Federated Tuning for Large Language Models |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671865)] |\n| CASA: Clustered Federated Learning with Asynchronous Clients |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671979)] |\n| FLAIM: AIM-based Synthetic Data Generation in the Federated Setting |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671990)] |\n| Privacy-Preserving Federated Learning using Flower Framework |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671447)] |\n| FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671748)] |\n| FedNLR: Federated Learning with Neuron-wise Learning Rates |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3672042)] |\n| FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671897)] |\n| FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671899)] |\n| Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671663)] |\n| PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671753)] |\n| FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671906)] |\n| FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671613)] |\n| OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671582)] |\n| Personalized Federated Continual Learning via Multi-Granularity Prompt |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671948)] |\n| Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671908)] |\n| GPFedRec: Graph-Guided Personalization for Federated Recommendation |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671702)] |\n| Asynchronous Vertical Federated Learning for Kernelized AUC Maximization |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671930)] |\n| VertiMRF: Differentially Private Vertical Federated Data Synthesis |  | KDD | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3637528.3671771)] |\n| User Consented Federated Recommender System Against Personalized Attribute Inference Attack | HKUST | WSDM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3616855.3635830)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.16203)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhkust-knowcomp\u002Fuc-fedrec)] |\n| Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning | ECNU | WSDM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3616855.3635758)] |\n| Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation | University of Cambridge | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599475)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.11050)] |\n| FedDefender: Client-Side Attack-Tolerant Federated Learning | KAIST | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599346)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09048)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdeu30303\u002Ffeddefender)] |\n| FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity | ZJU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599344)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhenqincn\u002FFedAPEN)] |\n| FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis | UMBC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599348)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.05247)] |\n| ShapleyFL: Robust Federated Learning Based on Shapley Value | ZJU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599500)] [[CODE](https:\u002F\u002Fgithub.com\u002FZJU-DIVER\u002FShapleyFL-Robust-Federated-Learning-Based-on-Shapley-Value)] |\n| Federated Few-shot Learning | University of Virginia | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599347)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.10234)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsongw-sw\u002Ff2l)] |\n| Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity | SDU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599521)] |\n| Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599464)] |\n| Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining | University of Pittsburgh | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599499)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03035)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxidongwu\u002FD-AUPRC)] |\n| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599293)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05613)] |\n| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599354)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03834)] |\n| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599345)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.01217)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedcp)] |\n| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599443)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.00489)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjiayunz\u002Ffedalign)] |\n| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599311)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaryzhang99\u002FDM-PFL)] |\n| FS-REAL: Towards Real-World Cross-Device Federated Learning | Alibaba Group | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599829)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13363)] |\n| FedMultimodal: A Benchmark for Multimodal Federated Learning | USC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599825)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.09486)] [[CODE](https:\u002F\u002Fgithub.com\u002Fusc-sail\u002Ffed-multimodal)] |\n| PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation | RUC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599889)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.08146)] [[NEWS](http:\u002F\u002Finfo.ruc.edu.cn\u002Fxwgg\u002Fxyxw\u002Fe4c838332c3a46cd8b959be49c021bb1.htm)] |\n| Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks | HKUST; Alibaba Group | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599898)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.01677)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Fbackdoor-bench)] |\n| UA-FedRec: Untargeted Attack on Federated News Recommendation | USTC | KDD | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599923)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.06701)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyjw1029\u002Fua-fedrec)] |\n| International Workshop on Federated Learning for Distributed Data Mining | MSU | KDD Workshop Summaries | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3580305.3599198)] [[PAGE](https:\u002F\u002Ffl4data-mining.github.io\u002F)] |\n| Is Normalization Indispensable for Multi-domain Federated Learning? |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=ZiaOEg8XiGN)] |\n| Distributed Personalized Empirical Risk Minimization. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=k2eYX1p-Yb)] |\n| Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=aJhe-VC0Ue)] |\n| SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=BVH3-XCRoN3)] |\n| Optimization of User Resources in Federated Learning for Urban Sensing Applications |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=D6ZQJ-szypI)] |\n| FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=nXjyCmLOYj)] |\n| Federated Graph Analytics with Differential Privacy. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=yBMbtNM3GR4)] |\n| Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=rAHB4qkWYz)] |\n| Uncertainty Quantification in Federated Learning for Heterogeneous Health Data |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=QSQOTUVQR46)] |\n| A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pLEQFXACNA)] |\n| Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=_Nsxwk3WWew)] |\n| Federated Blood Supply Chain Demand Forecasting: A Case Study. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=2c0hdQDvf5g)] |\n| Stochastic Clustered Federated Learning. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=pFvTwedsUh)] |\n| A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=jg3XzuNbS-0)] |\n| Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=W7LqmnU4TYZ)] |\n| FedNoisy: A Federated Noisy Label Learning Benchmark |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=cXMenagKy-7)] |\n| Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=DZvNrRNas6z)] |\n| Federated learning for competing risk analysis in healthcare. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=-HYSYe7uXRT)] |\n| Federated Threat Detection for Smart Home IoT rules. |  | KDD workshop | 2023 | [[PUB](https:\u002F\u002Fopenreview.net\u002Fforum?id=SK_KfAh8MtF)] |\n| Federated Unlearning for On-Device Recommendation | UQ | WSDM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539597.3570463)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10958)] |\n| Collaboration Equilibrium in Federated Learning | THU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539237)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.07926)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcuis15\u002Flearning-to-collaborate)] |\n| Connected Low-Loss Subspace Learning for a Personalization in Federated Learning | Ulsan National Institute of Science and Technology | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539254)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.07628)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvaseline555\u002Fsuperfed)] |\n| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks | University of Virginia | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539384)] |\n| Communication-Efficient Robust Federated Learning with Noisy Labels | University of Pittsburgh | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539328)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.05558)] |\n| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539231)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09209)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzaixizhang\u002FFLDetector)] |\n| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539402)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.08925)] [[CODE](https:\u002F\u002Fgithub.com\u002FDi-Chai\u002FFedEval)] |\n| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539308)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15896)] |\n| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning :fire: | Alibaba | KDD (Best Paper Award) | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539112)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.05562)] [[CODE](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope)] |\n| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539047)] [[PDF](https:\u002F\u002Fhufudb.com\u002Fstatic\u002Fpaper\u002F2022\u002FSIGKDD2022_Fed-LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F544183874)] |\n| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539039)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08036)] |\n| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539086)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08036)] |\n| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | THU | KDD | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539119)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.04975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwuch15\u002FFedAttack)] |\n| PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion | The University of Queensland | WSDM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3488560.3498386)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.10926)] |\n| Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467309)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14248)] |\n| FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Nanjing University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467254)] [[CODE](https:\u002F\u002Fgithub.com\u002Flxcnju\u002FFedRepo)] |\n| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467281)] [[PAGE](https:\u002F\u002Fjyhong.gitlab.io\u002Fpublication\u002Ffade2021kdd\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FFADE)] [[SLIDE](https:\u002F\u002Fjyhong.gitlab.io\u002Fpublication\u002Ffade2021kdd\u002Fslides.pdf)] |\n| Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | USC | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3447548.3467371)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmengcz13\u002FKDD2021_CNFGNN)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F434839878)] |\n| AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | Xidian University;JD Tech | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467169)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.12519)] |\n| FLOP: Federated Learning on Medical Datasets using Partial Networks | Duke University | KDD | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3447548.3467185)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05218.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjianyizhang123\u002FFLOP)] |\n| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3437963.3441702)] [[CODE](https:\u002F\u002Fgithub.com\u002FMTC-ETH\u002FFederated-Learning-source)] |\n| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3437963.3441747)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhxwujinze\u002Ffederated-deep-knowledge-tracing)] |\n| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | University College Dublin | KDD | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394486.3403176)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F23422)] |\n| Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | KDD | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394486.3403298)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06197)] [[VIDEO](https:\u002F\u002Fpapertalk.org\u002Fpapertalks\u002F23301)] |\n| Federated Online Learning to Rank with Evolution Strategies | Facebook AI Research | WSDM | 2019 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3289600.3290968)] [[CODE](http:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffoltr-es)] |\n\n\u003C!-- 结束：fl-在顶级安全会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n## 联邦学习在顶级安全会议和期刊中\n\n联邦学习相关论文已被顶级安全会议和期刊接收，包括 [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fsp\u002Findex.html)（IEEE 安全与隐私研讨会）、[CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fccs\u002Findex.html)（计算机与通信安全会议）、[USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fuss\u002Findex.html)（USENIX 安全研讨会）以及 [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fndss\u002Findex.html)（网络与分布式系统安全研讨会）。\n\n- [S&P](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsp%3A) [2025](https:\u002F\u002Fsp2025.ieee-security.org\u002Fprogram-papers.html), [2024](https:\u002F\u002Fsp2024.ieee-security.org\u002Fprogram-papers.html), [2023](https:\u002F\u002Fsp2023.ieee-security.org\u002Fprogram-papers.html), [2022](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2022\u002Fprogram-papers.html), [2019](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002Fprogram-papers.html)\n- [CCS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACCS%3A) [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3658644), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3576915), [2022](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2022\u002Fprogram\u002Faccepted-papers.html), [2021](https:\u002F\u002Fsigsac.org\u002Fccs\u002FCCS2021\u002Faccepted-papers.html), [2019](https:\u002F\u002Fwww.sigsac.org\u002Fccs\u002FCCS2019\u002Findex.php\u002Fprogram\u002Faccepted-papers\u002F), [2017](https:\u002F\u002Facmccs.github.io\u002Fpapers\u002F)\n- [USENIX Security](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fuss%3A) [2023](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Ftechnical-sessions), [2022](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Ftechnical-sessions), [2020](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Ftechnical-sessions)\n- [NDSS](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANDSS%3A) [2025](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2025\u002Faccepted-papers\u002F), [2024](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2024\u002Faccepted-papers\u002F), [2023](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2023\u002Faccepted-papers\u002F), [2022](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2022\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss2021\u002Faccepted-papers\u002F)\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级安全会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：fl-在顶级安全会议和期刊中 -->\n\n|标题                                                           |    机构                                                     |    会议    |    年份    |    文献|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ |\n| 并非所有边都同样稳健：评估基于排名的联邦学习的鲁棒性 |  | S&P | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11023255)] |\n| 在聚类联邦学习中，有限拜占庭客户端下的实用投毒攻击 |  | S&P | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11023464)] |\n| 一种用于实现隐私保护联邦学习的交互式框架：在大型语言模型上的实验 |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050826)] |\n| 智能交通系统中联邦学习的隐私保护双向认证协议 |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050805)] |\n| FedTilt：迈向多层级公平且鲁棒的联邦学习 |  | S&P Workshop | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11050846)] |\n| 用于增强语言模型联邦学习的隐私保护数据去重 |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fprivacy-preserving-data-deduplication-for-enhancing-federated-learning-of-language-models\u002F)] |\n| Scale-MIA：通过潜在空间重建对安全联邦学习的可扩展模型反演攻击 |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fscale-mia-a-scalable-model-inversion-attack-against-secure-federated-learning-via-latent-space-reconstruction\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Funknown123489\u002FScale-MIA)] |\n| URVFL：针对垂直联邦学习的不可检测数据重构攻击 |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Furvfl-undetectable-data-reconstruction-attack-on-vertical-federated-learning\u002F)] |\n| RAIFLE：利用对抗性数据操纵对基于交互的联邦学习进行重构攻击 |  | NDSS | 2025 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fraifle-reconstruction-attacks-on-interaction-based-federated-learning-with-adversarial-data-manipulation\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdzungvpham\u002Fraifle)] |\n| 拜占庭鲁棒的去中心化联邦学习 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670307)] |\n| 一个都不能少：探索用户画像与物品在针对联邦推荐系统的无目标攻击中的相互作用 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670365)] |\n| 基于记录级个性化差分隐私的跨silos联邦学习。 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3670351)] |\n| 可采样的匿名聚合用于隐私保护的联邦数据分析 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690224)] |\n| Camel：在差分隐私洗牌模型下通信高效且恶意安全的联邦学习 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690200)] |\n| 联邦图学习中的分布式后门攻击及认证防御 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690187)] |\n| 基于RLWE同态加密的两层数据打包用于安全联邦学习。 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3690191)] |\n| 海报：利用一元编码和打乱来防御联邦学习中的源推断攻击。 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3691411)] |\n| 海报：使用私有跨组织数据协作实现端到端隐私保护的垂直联邦学习。 |  | CCS | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3658644.3691383)] |\n| FP-Fed：隐私保护的浏览器指纹识别联邦检测 |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffp-fed-privacy-preserving-federated-detection-of-browser-fingerprinting\u002F)] |\n| FreqFed：基于频率分析的缓解联邦学习中投毒攻击的方法 |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffreqfed-a-frequency-analysis-based-approach-for-mitigating-poisoning-attacks-in-federated-learning\u002F)] |\n| 联邦学习中隐蔽投毒攻击的自动对抗适应 |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fautomatic-adversarial-adaption-for-stealthy-poisoning-attacks-in-federated-learning\u002F)] |\n| CrowdGuard：联邦学习中的后门检测 |  | NDSS | 2024 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fcrowdguard-federated-backdoor-detection-in-federated-learning\u002F)] |\n| 保护跨silos联邦学习中的标签分布 |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646748)] |\n| FLShield：一种基于验证的联邦学习框架，用于防御投毒攻击 |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646613)] |\n| BadVFL：垂直联邦学习中的后门攻击 |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646664)] |\n| SHERPA：面向未来网络的隐私保护联邦学习中可解释的鲁棒算法，用于防御数据投毒攻击 |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646830)] |\n| Loki：通过模型操纵对联邦学习的大规模数据重构攻击 |  | S&P | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10646724)] |\n| LayerDBA：绕过联邦学习中基于相似性的防御机制 |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10795458\u002F)] |\n| 海报：通过合成交互实现隐私保护的联邦推荐 |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10579513\u002F)] |\n| 机密联邦学习的性能分析 |  | S&P Workshop | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10579526)] |\n| 将隐私保护机制反过来用于攻击联邦学习 | 帕维亚大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623114)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05355)] |\n| MESAS：抵御自适应攻击者的联邦学习投毒防御 | 维尔茨堡大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623212)] |\n| martFL：通过稳健且可验证的联邦学习架构实现以效用为导向的数据市场 | 清华大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623134)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.01098)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliqi16\u002Fmartfl)] |\n| 揭示联邦学习中隐私与认证鲁棒性之间的联系，以抵抗投毒攻击 | 伊利诺伊大学厄巴纳-香槟分校 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623193)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04030)] |\n| 海报：在横向联邦学习中实现强公平性的可验证数据估值 | 国立成功大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3624371)] |\n| 海报：弥合信任鸿沟：联邦数据生态系统中的数据使用透明度 | 亚琛工业大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3624371)] |\n| 每一票都重要：基于排名的联邦学习训练以抵抗投毒攻击 | 马萨诸塞大学阿默斯特分校 | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fmozaffari)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04350)] |\n| PrivateFL：通过个性化数据转换实现准确且差分隐私保护的联邦学习 | 约翰霍普金斯大学 | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fyang-yuchen)] [[CODE](https:\u002F\u002Fgithub.com\u002FBHui97\u002FPrivateFL)] |\n| 梯度混淆在联邦学习中会带来虚假的安全感 | 北卡罗来纳州立大学 | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fyue)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.04055)] [[CODE](https:\u002F\u002Fgithub.com\u002FKAI-YUE\u002Frog)] |\n| FedVal：联邦学习中的不同好或不同坏 | AI Sweden | USENIX Security | 2023 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity23\u002Fpresentation\u002Fvaladi)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04040)] [[CODE](https:\u002F\u002Fgithub.com\u002Fviktorvaladi\u002Ffedval)] |\n| 保护联邦敏感话题分类免受投毒攻击 | IMDEA Networks Institute | NDSS | 2023 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fsecuring-federated-sensitive-topic-classification-against-poisoning-attacks\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.13086)] [[CODE](https:\u002F\u002Fgithub.com\u002FFRM-Sec\u002FFRM)] |\n| PPA：针对联邦学习的偏好画像攻击 | 南京理工大学 | NDSS | 2023 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fppa-preference-profiling-attack-against-federated-learning\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.04856)] |\n| 将隐私保护机制反过来用于攻击联邦学习 | 帕维亚大学、代尔夫特理工大学、帕多瓦大学、拉德博德大学 | CCS | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3576915.3623114)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.05355)] [[CODE](https:\u002F\u002Fgithub.com\u002FDCALab-UNIPV\u002FTurning-Privacy-preserving-Mechanisms-against-Federated-Learning)] |\n| CERBERUS：探索联邦安全事件预测 | 伦敦大学学院 | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560580)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.03050)] |\n| EIFFeL：确保联邦学习的完整性 | 威斯康星大学麦迪逊分校 | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560611)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.12727)] |\n| 通过模型不一致性逃避联邦学习中的安全聚合 | SPRING Lab；EPFL | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560557)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.07380)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpasquini-dario\u002Feludingsecureaggregation)] |\n| 差分隐私下的联邦提升决策树 | 沃里克大学 | CCS | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3548606.3560687)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02910)] [[CODE](https:\u002F\u002Fgithub.com\u002FSamuel-Maddock\u002Ffederated-boosted-dp-trees)] |\n| FedRecover：利用历史信息从联邦学习中的投毒攻击中恢复 | 杜克大学 | S&P | 2023 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2023\u002F933600a326\u002F1He7Y3q8FMY)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.10936)] |\n| 可扩展且隐私保护的联邦主成分分析 | EPFL；Tune Insight SA | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179350)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.00129)] |\n| SafeFL：适合多方计算的隐私且鲁棒的联邦学习框架 | 达姆施塔特工业大学 | S&P Workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10188630)] |\n| 关于鲁棒联邦学习安全性评估的陷阱 | 马萨诸塞大学 | S&P Workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10188636)] |\n| BayBFed：联邦学习的贝叶斯后门防御 | 达姆施塔特工业大学；UTSA | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179362)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.09508)] |\n| 3DFed：适用于联邦学习的自适应且可扩展的隐秘后门攻击框架 | 香港理工大学 | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179401)] [[CODE](https:\u002F\u002Fgithub.com\u002FhaoyangliASTAPLE\u002F3DFed)] |\n| RoFL：安全联邦学习的鲁棒性 | 苏黎世联邦理工学院 | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179400)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03311)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpps-lab\u002Frofl-project-code)] |\n| Flamingo：多轮单服务器安全聚合，适用于隐私保护的联邦学习。 | 宾夕法尼亚大学 | S&P | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10179434)] [[CODE](https:\u002F\u002Fgithub.com\u002Feniac\u002Fflamingo)] |\n| ELSA：适用于存在恶意参与者的联邦学习的安全聚合。 |  | S&P | 2023 |  |\n| 私密、高效且准确：利用差分隐私保护多方学习训练的模型 | 复旦大学 | S&P | 2023 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2023\u002F933600a076\u002F1He7XMLcnsc)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.08662)] |\n| 重新回到起点：对生产环境联邦学习中投毒攻击的批判性评估 | 马萨诸塞大学 | S&P | 2022 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9833647\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tQv3CpxIyvs)] |\n| SIMC：以半诚实成本实现对恶意客户端安全的机器学习推理 | 微软研究院 | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fchandran)] [[PDF](https:\u002F\u002Feprint.iacr.org\u002F2021\u002F1538)] [[CODE](https:\u002F\u002Fgithub.com\u002Fshahakash28\u002Fsimc)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0Oaqi0JHUac)] [[SUPP](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fusenixsecurity22-chandran.pdf)] |\n| 利用错误学习的困难性实现联邦学习中高效、差分隐私保护的安全聚合 | 佛蒙特大学 | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fstevens)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-stevens.pdf)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9kYHQkr6DuE)] |\n| 对垂直联邦学习的标签推断攻击 | 浙江大学 | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Ffu-chong)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-fu-chong.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FFuChong-cyber\u002Flabel-inference-attacks)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JEmRbDtosVw)] |\n| FLAME：驯服联邦学习中的后门 | 达姆施塔特工业大学 | USENIX Security | 2022 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity22\u002Fpresentation\u002Fnguyen)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec22_slides-nguyen.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.02281)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=nMrte2S9U68)] |\n| 局部和中央差分隐私用于联邦学习的鲁棒性和隐私 | 纽约州立大学布法罗分校 | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-204\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.03561)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_aH2j5A3608&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=3)] [[UC.](https:\u002F\u002Fgithub.com\u002Fwenzhu23333\u002FDifferential-Privacy-Based-Federated-Learning)] |\n| 可解释的联邦Transformer日志学习用于云威胁取证 | 圣言大学 | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-236\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3HoysA6hsC8&list=PLfUWWM-POgQsS08uHJUJI6sawDO_3sNh0&index=3)] [[UC.](https:\u002F\u002Fgithub.com\u002Fcyberthreat-datasets\u002Fctdd-2021-os-syslogs)] |\n| FedCRI：联邦移动网络风险情报 | 达姆施塔特工业大学 | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-229\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2zmdPqCCFxg&list=PLfUWWM-POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3)] |\n| DeepSight：通过深度模型检查缓解联邦学习中的后门攻击 | 达姆施塔特工业大学 | NDSS | 2022 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fauto-draft-205\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.00763)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MJF_7vnoGh4&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=4)] |\n| 联邦网络中的私密层次聚类 | 新加坡国立大学 | CCS | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460120.3484822)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09057)] |\n| FLTrust：通过信任引导实现拜占庭鲁棒的联邦学习 | 杜克大学 | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Ffltrust-byzantine-robust-federated-learning-via-trust-bootstrapping\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13995)] [[CODE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002Ffltrust.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zhhdPgKPCN0&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=2)] [[SLIDE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002FSecure_Federated_Learning.pdf)] |\n| POSEIDON：隐私保护的联邦神经网络学习 | EPFL | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fposeidon-privacy-preserving-federated-neural-network-learning\u002F)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kX6-PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1)] |\n| 操控拜占庭：优化联邦学习中的模型投毒攻击与防御 | 马萨诸塞大学阿默斯特分校 | NDSS | 2021 | [[PUB](https:\u002F\u002Fwww.ndss-symposium.org\u002Fndss-paper\u002Fmanipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning\u002F)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvrt1shjwlkr\u002FNDSS21-Model-Poisoning)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3)] |\n| SAFELearn：用于隐私保护联邦学习的安全聚合 | 达姆施塔特工业大学 | S&P Workshop | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9474309)] |\n| 局部模型投毒攻击至拜占庭鲁棒的联邦学习 | 俄亥俄州立大学 | USENIX Security | 2020 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fusenixsecurity20\u002Fpresentation\u002Ffang)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.11815)] [[CODE](https:\u002F\u002Fpeople.duke.edu\u002F~zg70\u002Fcode\u002Ffltrust.zip)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SQ12UpYrUVU&feature=emb_imp_woyt)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fsec20_slides_fang.pdf)] |\n| 使用区块链构建可靠且可问责的隐私保护联邦学习框架 | 堪萨斯大学 | CCS（海报） | 2019 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3319535.3363256)] |\n| IOTFLA：实施联邦学习的安全且隐私保护的智能家居架构 | 蒙特利尔魁北克大学 | S&P Workshop | 2019 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8844592)] |\n| 深度学习的全面隐私分析：针对集中式和联邦学习的被动与主动白盒推理攻击：🔥 | 马萨诸塞大学阿默斯特分校 | S&P | 2019 | [[PUB](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fproceedings-article\u002Fsp\u002F2019\u002F666000a739\u002F1dlwhtj4r7O)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FlzJY4BjCxTc)] [[SLIDE](https:\u002F\u002Fwww.ieee-security.org\u002FTC\u002FSP2019\u002FSP19-Slides-pdfs\u002FMilad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_)] [[CODE](https:\u002F\u002Fgithub.com\u002Fprivacytrustlab\u002Fml_privacy_meter)] |\n| 实用的安全聚合用于隐私保护的机器学习 | Google | CCS | 2017 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3133956.3133982)] [[PDF](https:\u002F\u002Feprint.iacr.org\u002F2017\u002F281)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F445656765)] [[UC.](https:\u002F\u002Fgithub.com\u002FChen-Junbao\u002FSecureAggregation)] [[UC](https:\u002F\u002Fgithub.com\u002Fcorentingiraud\u002Ffederated-learning-secure-aggregation)] |\n\n\u003C!-- 结束：顶级计算机视觉会议和期刊中的联邦学习 -->\n\n\u003C\u002Fdetails>\n\n\n\n\n## 顶级计算机视觉会议和期刊中的联邦学习\n\n被顶级计算机视觉（CV）会议和期刊接收的联邦学习论文，包括 [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fcvpr\u002Findex.html)（计算机视觉与模式识别）、[ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficcv\u002Findex.html)（IEEE 国际计算机视觉会议）、[ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Feccv\u002Findex.html)（欧洲计算机视觉会议）、[MM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fmm\u002Findex.html)（ACM 国际多媒体会议）、[IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fjournals\u002Fijcv\u002Findex.html)（国际计算机视觉杂志）。\n\n- [CVPR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACVPR%3A) [2025](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2025?day=all)、[2024](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2024?day=all)、[2023](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2023?day=all)、[2022](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2022)、[2021](https:\u002F\u002Fopenaccess.thecvf.com\u002FCVPR2021?day=all)\n- [ICCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICCV%3A) [2023](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2023?day=all)、[2021](https:\u002F\u002Fopenaccess.thecvf.com\u002FICCV2021?day=all)\n- [ECCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AECCV%3A) [2024](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php)、[2022](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php)、[2020](https:\u002F\u002Fwww.ecva.net\u002Fpapers.php)\n- [MM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fmm%3A) [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3664647)、[2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3581783)、[2022](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fmm\u002Fmm2022.html)、[2021](https:\u002F\u002F2021.acmmm.org\u002Fmain-track-list)、[2020](https:\u002F\u002F2020.acmmm.org\u002Fmain-track-list.html)\n- [IJCV](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20streamid%3Ajournals%2Fijcv%3A) 2025、2024\n\n\u003Cdetails open>\n\u003Csummary>顶级计算机视觉会议和期刊中的联邦学习\u003C\u002Fsummary>\n\n\u003C!-- 开始：顶级计算机视觉会议和期刊中的联邦学习 -->\n\n|Title                                                           |    Affiliation                                                     |    Venue    |    Year    |    Materials|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ |\n| Federated Learning with Domain Shift Eraser |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FWang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.html)] |\n| Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FCaldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fpietrocagnasso\u002Ffedgloss)] |\n| FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FChen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaokunChen245\u002FFedBiP)] |\n| FedCS: Coreset Selection for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FHao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.html)] |\n| AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FHe_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html)] |\n| NoT: Federated Unlearning via Weight Negation |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FKhalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.html)] |\n| Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FKumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.html)] |\n| Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FLi_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html)] |\n| Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FLiu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FJay-Codeman\u002FSAGE)] |\n| Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FMa_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html)] |\n| HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FRaswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.html)] [[COCE](https:\u002F\u002Flalakitchen.github.io\u002FHistoFS\u002F)] |\n| F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective  Meta-Heuristics |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FSaha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.html)] [[PAGE](https:\u002F\u002Fpramitsaha.github.io\u002FFOCUS\u002F)] |\n| FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FShi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html)] |\n| FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FTan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FOakleyTan\u002FFedSPA)] |\n| Population Normalization for Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FWang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.html)] |\n| Model Poisoning Attacks to Federated Learning via Multi-Round Consistency |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxyq7\u002FPoisonedFL\u002F)] |\n| dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.html)] |\n| Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FXu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FAlignIns)] |\n| A Simple Data Augmentation for Feature Distribution Skewed Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FIAMJackYan\u002FFedRDN)] |\n| Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html)] |\n| Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FAVC2-UESTC\u002FFedSCE.git)] |\n| pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.html)] |\n| FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.html)] |\n| Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.html)] |\n| FedMIA: An Effective Membership Inference Attack Exploiting \"All for One\" Principle in Federated Learning |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FZhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiar-Mask\u002FFedMIA)] |\n| Patient-Level Anatomy Meets Scanning-Level Physics: Personalized  Federated Low-Dose CT Denoising Empowered by Large Language Model |  | CVPR | 2025 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2025\u002Fhtml\u002FYang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.html)] |\n| Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning |  | IJCV | 2025 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-024-02330-1)] |\n| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681260)] |\n| One-shot-but-not-degraded Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680715)] |\n| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681384)] |\n| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681490)] |\n| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681588)] |\n| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680867)] |\n| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680733)] |\n| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681545)] |\n| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681054)] |\n| FedSLS: Exploring Federated Aggregation in Saliency Latent Space |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681278)] |\n| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680788)] |\n| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3664647.3681094)] |\n| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data |  | MM | 2024 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3664647.3681480)] |\n| Cross-Modal Meta Consensus for Heterogeneous Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681510)] |\n| Masked Random Noise for Communication-Efficient Federated Learning |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3680608)] |\n| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681302)] |\n| Adaptive Hierarchical Aggregation for Federated Object Detection |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681158)] |\n| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681319)] |\n| Federated Fuzzy C-means with Schatten-p Norm Minimization |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681557)] |\n| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation |  | MM | 2024 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3664647.3681415)] |\n| Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification |  | IJCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-024-02077-9)] |\n| SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72655-2_17)] [[CODE](https:\u002F\u002Fgithub.com\u002FKoalaYan\u002FSkyMask)] |\n| FedHide: Federated Learning by Hiding in the Neighbors |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72897-6_23)] |\n| FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73668-1_14)] |\n| FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73195-2_20)] |\n| Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73030-6_10)] |\n| Federated Learning with Local Openset Noisy Labels |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72754-2_3)] |\n| FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73010-8_22)] |\n| Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73004-7_11)] |\n| BAFFLE: A Baseline of Backpropagation-Free Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73226-3_6)] |\n| PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73650-6_9)] |\n| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72633-0_14)] |\n| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73229-4_2)] |\n| FedHARM: Harmonizing Model Architectural Diversity in Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73036-8_3)] |\n| SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72986-7_10)] |\n| Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72952-2_8)] |\n| Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73404-5_18)] |\n| Towards Multi-modal Transformers in Federated Learning |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72633-0_13)] |\n| Local and Global Flatness for Federated Domain Generalization |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73010-8_5)] |\n| Feature Diversification and Adaptation for Federated Domain Generalization |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-73220-1_4)] |\n| PFEDEDIT: Personalized Federated Learning via Automated Model Editing |  | ECCV | 2024 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-72986-7_6)] |\n| FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | SJTU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLu_FedHCA2_Towards_Hetero-Client_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.13250)] [[CODE](https:\u002F\u002Fgithub.com\u002Finnovator-zero\u002FFedHCA2)] |\n| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | WHU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Fair_Federated_Learning_under_Domain_Skew_with_Local_Consistency_and_CVPR_2024_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.16585)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuhangchen0\u002FFedHEAL)] |\n| Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts | NWPU; HKUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Think_Twice_Before_Selection_Federated_Evidential_Active_Learning_for_Medical_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FChen_Think_Twice_Before_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2312.02567)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiayiChen815\u002FFEAL)] |\n| FedMef: Towards Memory-efficient Federated Dynamic Pruning | CUHK | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FHuang_FedMef_Towards_Memory-efficient_Federated_Dynamic_Pruning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FHuang_FedMef_Towards_Memory-efficient_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14737)] |\n| Communication-Efficient Federated Learning with Accelerated Client Gradient | SNU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FKim_Communication-Efficient_Federated_Learning_with_Accelerated_Client_Gradient_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FKim_Communication-Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2201.03172)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgeehokim\u002FFedACG)] |\n| Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space | IITH | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FKumar_Revamping_Federated_Learning_Security_from_a_Defenders_Perspective_A_Unified_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FKumar_Revamping_Federated_Learning_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FNaveenKumar-1311\u002FFCD)] |\n| Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning | TJUT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FQi_Adaptive_Hyper-graph_Aggregation_for_Modality-Agnostic_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FQi_Adaptive_Hyper-graph_Aggregation_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FMM-Fed\u002FHAMFL)] |\n| Towards Efficient Replay in Federated Incremental Learning | HUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Towards_Efficient_Replay_in_Federated_Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Towards_Efficient_Replay_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.05890)] |\n| Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices | UT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FChen_Mixed-Precision_Quantization_for_Federated_Learning_on_Resource-Constrained_Heterogeneous_Devices_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FChen_Mixed-Precision_Quantization_for_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2311.18129)] |\n| Data Valuation and Detections in Federated Learning | NUS | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Data_Valuation_and_Detections_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Data_Valuation_and_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05304)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmuz1lee\u002Fmotdata)] |\n| Decentralized Directed Collaboration for Personalized Federated Learning | NJUST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLiu_Decentralized_Directed_Collaboration_for_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLiu_Decentralized_Directed_Collaboration_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.17876)] |\n| Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning | UBC | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FDeng_Unlocking_the_Potential_of_Prompt-Tuning_in_Bridging_Generalized_and_Personalized_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FDeng_Unlocking_the_Potential_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18285)] [[CODE](https:\u002F\u002Fgithub.com\u002Fubc-tea\u002FSGPT)] |\n| Global and Local Prompts Cooperation via Optimal Transport for Federated Learning | ShanghaiTech University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLi_Global_and_Local_Prompts_Cooperation_via_Optimal_Transport_for_Federated_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLi_Global_and_Local_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.00041)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhongxialee\u002Ffedotp)] |\n| Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data | ZJU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLiao_Rethinking_the_Representation_in_Federated_Unsupervised_Learning_with_Non-IID_Data_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLiao_Rethinking_the_Representation_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.16398)] [[CODE](https:\u002F\u002Fgithub.com\u002FXeniaLLL\u002FFedU2)] |\n| Relaxed Contrastive Learning for Federated Learning | SNU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSeo_Relaxed_Contrastive_Learning_for_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSeo_Relaxed_Contrastive_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2401.04928)] [[CODE](https:\u002F\u002Fgithub.com\u002Fskynbe\u002FFedRCL)] |\n| Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning | Purdue University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhao_Leak_and_Learn_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.18144)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ovmSnjSOcks)] |\n| Traceable Federated Continual Learning | BUPT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FWang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FWang_Traceable_Federated_Continual_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FP0werWeirdo\u002FTagFCL)] |\n| Federated Online Adaptation for Deep Stereo | University of Bologna | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FPoggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FPoggi_Federated_Online_Adaptation_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.14873)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmattpoggi\u002Ffedstereo)] [[PAGE](https:\u002F\u002Ffedstereo.github.io\u002F)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FgVpWsjrUTJc)] |\n| Federated Generalized Category Discovery | UniTn | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FPu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FPu_Federated_Generalized_Category_CVPR_2024_supplemental.zip)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14107)] [[CODE](https:\u002F\u002Fgithub.com\u002FTPCD\u002FFedGCD)] |\n| Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization | ND | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLe_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLe_Efficiently_Assemble_Normalization_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.15605)] [[CODE](https:\u002F\u002Fgithub.com\u002Flhkhiem28\u002FgPerXAN?utm_source=catalyzex.com)] |\n| Text-Enhanced Data-free Approach for Federated Class-Incremental Learning | Monash University | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FTran_Text-Enhanced_Data-free_Approach_for_Federated_Class-Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FTran_Text-Enhanced_Data-free_Approach_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14101)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftmtuan1307\u002Flander)] |\n| PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | UIUC; NVIDIA | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FXie_PerAda_Parameter-Efficient_Federated_Learning_Personalization_with_Generalization_Guarantees_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FXie_PerAda_Parameter-Efficient_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06637)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FPerAda)] |\n| FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning | KAIST | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FLee_FedSOL_Stabilized_Orthogonal_Learning_with_Proximal_Restrictions_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FLee_FedSOL_Stabilized_Orthogonal_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.12532)] [[CODE](https:\u002F\u002Fgithub.com\u002FLee-Gihun\u002FFedSOL)] |\n| FedUV: Uniformity and Variance for Heterogeneous Federated Learning | UC Davis | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSon_FedUV_Uniformity_and_Variance_for_Heterogeneous_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSon_FedUV_Uniformity_and_CVPR_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2402.18372)] |\n| FedAS: Bridging Inconsistency in Personalized Federated Learning | WHU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FYang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxiyuanyang45\u002FFedAS)] |\n| FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Lapis Labs | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FTamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FTamirisa_FedSelect_Personalized_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.02478)] [[CODE](https:\u002F\u002Fgithub.com\u002Flapisrocks\u002Ffedselect)] |\n| Device-Wise Federated Network Pruning | PITT | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FGao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FGao_Device-Wise_Federated_Network_CVPR_2024_supplemental.pdf)] |\n| Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping | HNU; PolyU; AIRS | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FSun_Byzantine-robust_Decentralized_Federated_Learning_via_Dual-domain_Clustering_and_Trust_Bootstrapping_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FSun_Byzantine-robust_Decentralized_Federated_CVPR_2024_supplemental.pdf)] |\n| DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning | HKUST; PolyU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FBai_DiPrompT_Disentangled_Prompt_Tuning_for_Multiple_Latent_Domain_Generalization_in_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FBai_DiPrompT_Disentangled_Prompt_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.08506)] |\n| An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning | SJTU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhang_An_Upload-Efficient_Scheme_for_Transferring_Knowledge_From_a_Server-Side_Pre-trained_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhang_An_Upload-Efficient_Scheme_CVPR_2024_supplemental.zip)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.15760)] [[CODE](https:\u002F\u002Fgithub.com\u002Ftsingz0\u002Ffedktl)] [[POSTER](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedKTL\u002Fblob\u002Fmain\u002FFedKTL.png)] [[SLIDES](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FFedKTL\u002Fblob\u002Fmain\u002FFedKTL.pdf)] |\n| An Aggregation-Free Federated Learning for Tackling Data Heterogeneity | A* STAR | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FWang_An_Aggregation-Free_Federated_Learning_for_Tackling_Data_Heterogeneity_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FWang_An_Aggregation-Free_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.18962)] |\n| FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning | BUAA; HKU | CVPR | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fhtml\u002FZhang_FLHetBench_Benchmarking_Device_and_State_Heterogeneity_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024\u002Fsupplemental\u002FZhang_FLHetBench_Benchmarking_Device_CVPR_2024_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FCarkham\u002FFLHetBench)] [[PAGE](https:\u002F\u002Fcarkham.github.io\u002FFL_Het_Bench\u002F)] [[POSTER](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1Ln0cnptSn5EfML6ughQ7NowwjjLfMYgu\u002Fview?usp=sharing)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zDGPt3929l8)] |\n| Collaborative Visual Place Recognition through Federated Learning |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FDutto_Collaborative_Visual_Place_Recognition_through_Federated_Learning_CVPRW_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fsupplemental\u002FDutto_Collaborative_Visual_Place_CVPRW_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2404.13324)] |\n| FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FGao_FedProK_Trustworthy_Federated_Class-Incremental_Learning_via_Prototypical_Feature_Knowledge_Transfer_CVPRW_2024_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fsupplemental\u002FGao_FedProK_Trustworthy_Federated_CVPRW_2024_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2405.02685)] |\n| Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FNakka_Federated_Hyperparameter_Optimization_Through_Reward-Based_Strategies_Challenges_and_Insights_CVPRW_2024_paper.html)] |\n| On the Efficiency of Privacy Attacks in Federated Learning |  | CVPR workshop | 2024 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2024W\u002FFedVision-2024\u002Fhtml\u002FTabassum_On_the_Efficiency_of_Privacy_Attacks_in_Federated_Learning_CVPRW_2024_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2404.09430)] |\n| FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612217)] |\n| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611958)] |\n| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612597)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.02416)] [[CODE](https:\u002F\u002Fgithub.com\u002FJ1nqianChen\u002FFedKA)] |\n| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612027)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13697)] |\n| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611953)] |\n| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3613837)] [[CODE](https:\u002F\u002Fgithub.com\u002Fexquisite1210\u002FPT-FUCH_P)] |\n| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612178)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.11646)] |\n| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611966)] |\n| Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611984)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwnma3mz\u002FFedLMD)] |\n| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612481)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03457)] [[CODE](https:\u002F\u002Fgithub.com\u002Fqizhuang-qz\u002FFedCSPC)] |\n| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612474)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03331)] |\n| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611757)] |\n| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3611781)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13137)] [[CODE](https:\u002F\u002Fgithub.com\u002FLipingYi\u002FFedGH)] |\n| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612134)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03432)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyuxuanzhang0713\u002Ffedcsr)] |\n| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612442)] [[CODE](https:\u002F\u002Fgithub.com\u002FAffectFAL\u002FAffectFAL)] |\n| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3581783.3612350)] |\n| Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHan_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09318)] [[CODE](https:\u002F\u002Fgithub.com\u002FSungwon-Han\u002FFEDCPA)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHan_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] |\n| Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; NVIDIA | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FYang_Efficient_Model_Personalization_in_Federated_Learning_via_Client-Specific_Prompt_Generation_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.15367)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FYang_Efficient_Model_Personalization_ICCV_2023_supplemental.pdf)] |\n| Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fczhang024\u002FCI-Net)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] |\n| GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning | SJTU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_GPFL_Simultaneously_Learning_Global_and_Personalized_Feature_Information_for_Personalized_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.10279)] [[CODE](https:\u002F\u002Fgithub.com\u002FTsingZ0\u002FGPFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhang_GPFL_Simultaneously_Learning_ICCV_2023_supplemental.zip)] |\n| Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization | University of Houston | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FChen_Workie-Talkie_Accelerating_Federated_Learning_by_Overlapping_Computing_and_Communications_via_ICCV_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FChen_Workie-Talkie_Accelerating_Federated_ICCV_2023_supplemental.pdf)] |\n| PGFed: Personalize Each Client's Global Objective for Federated Learning | University of Pittsburgh | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FLuo_PGFed_Personalize_Each_Clients_Global_Objective_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01448)] [[CODE](https:\u002F\u002Fgithub.com\u002Fljaiverson\u002Fpgfed)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FLuo_PGFed_Personalize_Each_ICCV_2023_supplemental.pdf)] |\n| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FSun_FedPerfix_Towards_Partial_Model_Personalization_of_Vision_Transformers_in_Federated_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09160)] [[CODE](https:\u002F\u002Fgithub.com\u002Fimguangyu\u002Ffedperfix)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FSun_FedPerfix_Towards_Partial_ICCV_2023_supplemental.pdf)] |\n| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FRehman_L-DAWA_Layer-wise_Divergence_Aware_Weight_Aggregation_in_Federated_Self-Supervised_Visual_ICCV_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.07393)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FRehman_L-DAWA_Layer-wise_Divergence_ICCV_2023_supplemental.pdf)] |\n| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FYang_FedPD_Federated_Open_Set_Recognition_with_Parameter_Disentanglement_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FCityU-AIM-Group\u002FFedPD)] |\n| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhang_TARGET_Federated_Class-Continual_Learning_via_Exemplar-Free_Distillation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06937)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzj-jayzhang\u002FFederated-Class-Continual-Learning)] |\n| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FFeng_Towards_Instance-adaptive_Inference_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2308.06051)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchunmeifeng\u002Ffedins)] |\n| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhou_Communication-efficient_Federated_Learning_with_Single-Step_Synthetic_Features_Compressor_for_Faster_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2302.13562)] [[CODE](https:\u002F\u002Fgithub.com\u002FSoptq\u002Ficcv23-3sfc)] |\n| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FGhodsi_zPROBE_Zero_Peek_Robustness_Checks_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2206.12100)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FGhodsi_zPROBE_Zero_Peek_ICCV_2023_supplemental.pdf)] |\n| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FKim_ProtoFL_Unsupervised_Federated_Learning_via_Prototypical_Distillation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.12450)] |\n| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZhuang_MAS_Towards_Resource-Efficient_Federated_Multiple-Task_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11285)] [[CODE](https:\u002F\u002Fgithub.com\u002FEasyFL-AI\u002FEasyFL\u002Ftree\u002Fmaster\u002Fapplications\u002Fmas)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZhuang_MAS_Towards_Resource-Efficient_ICCV_2023_supplemental.pdf)] |\n| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FGuo_FSAR_Federated_Skeleton-based_Action_Recognition_with_Adaptive_Topology_Structure_and_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2306.11046)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FGuo_FSAR_Federated_Skeleton-based_ICCV_2023_supplemental.pdf)] |\n| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FVahidian_When_Do_Curricula_Work_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2212.12712)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FVahidian_When_Do_Curricula_ICCV_2023_supplemental.pdf)] |\n| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Duke University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FSun_Communication-Efficient_Vertical_Federated_Learning_with_Limited_Overlapping_Samples_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16270)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare\u002Ftree\u002Fmain\u002Fresearch\u002Fone-shot-vfl)] |\n| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | SCUT | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHuang_Multi-Metrics_Adaptively_Identifies_Backdoors_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06601)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsiquanhuang\u002FMulti-metrics_against_backdoors_in_FL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHuang_Multi-Metrics_Adaptively_Identifies_ICCV_2023_supplemental.pdf)] |\n| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | ZJU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FLi_No_Fear_of_Classifier_Biases_Neural_Collapse_Inspired_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10058)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzexilee\u002Ficcv-2023-fedetf)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FLi_No_Fear_of_ICCV_2023_supplemental.pdf)] |\n| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation | Ludwig Maximilian University of Munich; Siemens Technology | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FChen_FRAug_Tackling_Federated_Learning_with_Non-IID_Features_via_Representation_Augmentation_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.14900)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FChen_FRAug_Tackling_Federated_ICCV_2023_supplemental.pdf)] |\n| Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | BUAA | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FWu_Bold_but_Cautious_Unlocking_the_Potential_of_Personalized_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2309.11103)] [[CODE](https:\u002F\u002Fgithub.com\u002Fkxzxvbk\u002FFling)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FWu_Bold_but_Cautious_ICCV_2023_supplemental.pdf)] |\n| Global Balanced Experts for Federated Long-Tailed Learning | CUHK-Shenzhen | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FZeng_Global_Balanced_Experts_for_Federated_Long-Tailed_Learning_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FSpinozaaa\u002FFederated-Long-tailed-Learning)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FZeng_Global_Balanced_Experts_ICCV_2023_supplemental.pdf)] |\n| Knowledge-Aware Federated Active Learning with Non-IID Data | The University of Sydney | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FCao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13579)] [[CODE](https:\u002F\u002Fgithub.com\u002Fycao5602\u002FKAFAL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FCao_Knowledge-Aware_Federated_Active_ICCV_2023_supplemental.pdf)] |\n| Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation | BUPT | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FWan_Enhancing_Privacy_Preservation_in_Federated_Learning_via_Learning_Rate_Perturbation_ICCV_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FWan_Enhancing_Privacy_Preservation_ICCV_2023_supplemental.pdf)] |\n| Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels | CMU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FCho_Local_or_Global_Selective_Knowledge_Assimilation_for_Federated_Learning_with_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2307.08809)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FCho_Local_or_Global_ICCV_2023_supplemental.pdf)] |\n| Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat | Rice University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FHu_Federated_Learning_Over_Images_Vertical_Decompositions_and_Pre-Trained_Backbones_Are_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2309.03237)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhuerdong\u002FFedVert-Experiments)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FHu_Federated_Learning_Over_ICCV_2023_supplemental.pdf)] |\n| Robust Heterogeneous Federated Learning under Data Corruption | WHU | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FFang_Robust_Heterogeneous_Federated_Learning_under_Data_Corruption_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FFangXiuwen\u002FAugHFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FFang_Robust_Heterogeneous_Federated_ICCV_2023_supplemental.pdf)] |\n| Personalized Semantics Excitation for Federated Image Classification | Tulane University | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FXia_Personalized_Semantics_Excitation_for_Federated_Image_Classification_ICCV_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FHaifengXia\u002FPSE)] |\n| Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology | AIOZ | ICCV | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fhtml\u002FDo_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09657)] [[CODE](https:\u002F\u002Fgithub.com\u002Faioz-ai\u002FMultigraphFL)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2023\u002Fsupplemental\u002FDo_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_supplemental.pdf)] |\n| Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. | Politecnico di Torino | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350693)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01366)] |\n| Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. | University of Catania | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350429)] |\n| FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350898)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchatzikon\u002FFedRCIL)] |\n| FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10350371)] |\n| Rethinking Federated Learning With Domain Shift: A Prototype View | WHU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FHuang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FRethinkFL)] |\n| Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning | ECNU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fminglllli\u002FCBAFed)] |\n| DaFKD: Domain-Aware Federated Knowledge Distillation | HUST | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FWang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhaozhaowang\u002FDaFKD2023)] |\n| The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Purdue University | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.14868)] |\n| FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation | ZJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FMiao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html)] |\n| On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | DTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLi_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02191)] |\n| Elastic Aggregation for Federated Optimization | Meituan | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FChen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html)] |\n| FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | UCLA | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FXiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09653)] |\n| Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | UM | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLiao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html)] |\n| ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | GaTech | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FIlhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002Fscale-fl)] |\n| Reliable and Interpretable Personalized Federated Learning | TJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FQin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html)] |\n| Federated Domain Generalization With Generalization Adjustment | SJTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FFedDG-GA)] |\n| Make Landscape Flatter in Differentially Private Federated Learning | THU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FShi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.11242)] [[CODE](https:\u002F\u002Fgithub.com\u002FYMJS-Irfan\u002FDP-FedSAM)] |\n| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Leuven | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FZhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12557)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjunyizhu-ai\u002Fconfidence_aware_pfl)] |\n| STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | GaTech | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FChow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.11511)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgit-disl\u002FSTDLens)] |\n| Re-Thinking Federated Active Learning Based on Inter-Class Diversity | KAIST | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FKim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.12317)] [[CODE](https:\u002F\u002Fgithub.com\u002Fraymin0223\u002FLoGo)] |\n| Learning Federated Visual Prompt in Null Space for MRI Reconstruction | A*STAR | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FFeng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16181)] [[CODE](https:\u002F\u002Fgithub.com\u002Fchunmeifeng\u002FFedPR)] |\n| Fair Federated Medical Image Segmentation via Client Contribution Estimation | CUHK | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FJiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16520)] [[CODE](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare\u002Ftree\u002Fdev\u002Fresearch\u002Ffed-ce)] |\n| Federated Learning With Data-Agnostic Distribution Fusion | NJU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FDuan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiruichenSpace\u002FFedFusion)] |\n| How To Prevent the Poor Performance Clients for Personalized Federated Learning? | CSU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FQu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html)] |\n| GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | ECNU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FLuo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2302.14307)] [[CODE](https:\u002F\u002Fgithub.com\u002Flkyddd\u002Fgradma)] |\n| Bias-Eliminating Augmentation Learning for Debiased Federated Learning | NTU | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FXu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.html)] |\n| Federated Incremental Semantic Segmentation | CAS; UCAS | CVPR | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fhtml\u002FDong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04620)] [[CODE](https:\u002F\u002Fgithub.com\u002FJiahuaDong\u002FFISS)] |\n| Asynchronous Federated Continual Learning | University of Padova | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FShenaj_Asynchronous_Federated_Continual_Learning_CVPRW_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03626)] [[SILDES](https:\u002F\u002Fgithub.com\u002FLTTM\u002FFedSpace\u002Fblob\u002Fmain\u002Fmedia\u002Fslides.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FLTTM\u002FFedSpace)] |\n| Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks | UMBC | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FOvi_Mixed_Quantization_Enabled_Federated_Learning_To_Tackle_Gradient_Inversion_Attacks_CVPRW_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FPretomRoy\u002FDefense-against-grad-inversion-attacks)] |\n| OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework | Meituan | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FChen_OpenFed_A_Comprehensive_and_Versatile_Open-Source_Federated_Learning_Framework_CVPRW_2023_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.07852)] [[CODE](https:\u002F\u002Fgithub.com\u002FFederalLab\u002FOpenFed)] |\n| Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | utexas | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FChen_Federated_Learning_in_Non-IID_Settings_Aided_by_Differentially_Private_Synthetic_CVPRW_2023_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fsupplemental\u002FChen_Federated_Learning_in_CVPRW_2023_supplemental.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00686)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcitychan\u002Ffederated-dpms)] |\n| TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training | USC | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FZhang_TimelyFL_Heterogeneity-Aware_Asynchronous_Federated_Learning_With_Adaptive_Partial_Training_CVPRW_2023_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2304.06947)] |\n| Many-Task Federated Learning: A New Problem Setting and a Simple Baseline | utexas | CVPR workshop | 2023 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023W\u002FFedVision\u002Fhtml\u002FCai_Many-Task_Federated_Learning_A_New_Problem_Setting_and_a_Simple_CVPRW_2023_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002FVITA-Group\u002FMaT-FL)] |\n| Confederated Learning: Going Beyond Centralization | CAS;  UCAS | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548157)] |\n| Few-Shot Model Agnostic Federated Learning | WHU | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548764)] [[CODE](https:\u002F\u002Fgithub.com\u002FWenkeHuang\u002FFSMAFL)] |\n| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | MM | 2022 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3503161.3548278)] |\n| FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6634_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136720069-supp.pdf)] |\n| Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F1129_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136810431-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.06338)] [[CODE](https:\u002F\u002Fgithub.com\u002Fguopengf\u002FAuto-FedRL)] |\n| Improving Generalization in Federated Learning by Seeking Flat Minima | Politecnico di Torino | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F7093_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136830636-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11834)] [[CODE](https:\u002F\u002Fgithub.com\u002Fdebcaldarola\u002Ffedsam)] |\n| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F8092_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136830690-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13170)] [[CODE](https:\u002F\u002Fgithub.com\u002Fvarnio\u002Ffedsim)] [[PAGE](https:\u002F\u002Ffedsim.varnio.com\u002Fen\u002Flatest\u002F)] |\n| SphereFed: Hyperspherical Federated Learning |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F2255_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136860161-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09413)] |\n| Federated Self-Supervised Learning for Video Understanding |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F7693_ECCV_2022_paper.php)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.01975)] [[CODE](https:\u002F\u002Fgithub.com\u002Fyasar-rehman\u002Ffedvssl)] |\n| FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6298_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136960673-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14936)] [[CODE](https:\u002F\u002Fgithub.com\u002Feric-ai-lab\u002FFedVLN)] |\n| Addressing Heterogeneity in Federated Learning via Distributional Transformation |  | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F6551_ECCV_2022_paper.php)] [[CODE](https:\u002F\u002Fgithub.com\u002Fhyhmia\u002FDisTrans)] |\n| FedX: Unsupervised Federated Learning with Cross Knowledge Distillation | KAIST | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F3932_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136900682-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.09158)] [[CODE](https:\u002F\u002Fgithub.com\u002Fsungwon-han\u002Ffedx)] |\n| Personalizing Federated Medical Image Segmentation via Local Calibration | Xiamen University | ECCV | 2022 | [[PUB](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fhtml\u002F1626_ECCV_2022_paper.php)] [[SUPP](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136810449-supp.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.04655)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjcwang123\u002Ffedlc)] |\n| ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | HIT | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FWang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html)] |\n| Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Stanford | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FQu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FQu_Rethinking_Architecture_Design_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06047)] [[CODE](https:\u002F\u002Fgithub.com\u002FLiangqiong\u002FViT-FL-main)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ae1CDi0_Nok&ab_channel=StanfordMedAI)] |\n| FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Singapore University of Technology and Design | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FXu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FXu_FedCorr_Multi-Stage_Federated_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04677)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxu-jingyi\u002Ffedcorr)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GA22ct1LgRA&ab_channel=ZihanChen)] |\n| FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Duke University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FTang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FTang_FedCor_Correlation-Based_Active_CVPR_2022_supplemental.zip)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2103.13822)] |\n| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FMa_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FMa_Layer-Wised_Model_Aggregation_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03993)] |\n| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FMendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FMendieta_Local_Learning_Matters_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2111.14213)] [[CODE](https:\u002F\u002Fgithub.com\u002Fmmendiet\u002FFedAlign)] |\n| Federated Learning With Position-Aware Neurons | Nanjing University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLi_Federated_Learning_With_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14666)] |\n| RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning | HKUST | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLiang_RSCFed_Random_Sampling_Consensus_Federated_Semi-Supervised_Learning_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLiang_RSCFed_Random_Sampling_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.13993)] [[CODE](https:\u002F\u002Fgithub.com\u002Fxmed-lab\u002Frscfed)] |\n| Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wuhan University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FHuang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.html)] [[CODE](https:\u002F\u002Fgithub.com\u002Fwenkehuang\u002Ffccl)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zZoASA71qwQ&ab_channel=HuangWenke)] |\n| Robust Federated Learning With Noisy and Heterogeneous Clients | Wuhan University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FFang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FFang_Robust_Federated_Learning_CVPR_2022_supplemental.pdf)] [[CODE](https:\u002F\u002Fgithub.com\u002FFangXiuwen\u002FRobust_FL)] |\n| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Arizona State University | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html)] [[SUPP](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fsupplemental\u002FLi_ResSFL_A_Resistance_CVPR_2022_supplemental.pdf)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2205.04007)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzlijingtao\u002FResSFL)] |\n| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FGao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11751)] [[CODE](https:\u002F\u002Fgithub.com\u002Fgaoliang13\u002FFedDC)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F505889549)] |\n| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FDong_Federated_Class-Incremental_Learning_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11473)] [[CODE](https:\u002F\u002Fgithub.com\u002FconditionWang\u002FFCIL)] |\n| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU;  JD Explore Academy;  The University of Sydney | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FZhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09249)] |\n| Differentially Private Federated Learning With Local Regularization and Sparsification | CAS | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FCheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03106)] |\n| Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage | University of Tennessee; Oak Ridge National Laboratory; Google Research | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FLi_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.15696)] [[CODE](https:\u002F\u002Fgithub.com\u002Fzhuohangli\u002FGGL)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rphFSGDlGPY&ab_channel=MoSISLab)] |\n| CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | SJTU | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FShen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03880)] |\n| Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | Univ. of Pittsburgh; NVIDIA | CVPR | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fhtml\u002FXu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.html)] [[PDF](http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.10144)] |\n| Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | HHI | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FBecking_Adaptive_Differential_Filters_for_Fast_and_Communication-Efficient_Federated_Learning_CVPRW_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.04424)] [[SILDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002FBecking_FSFL_FedVision_CVPR22.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FA9nEWqGriZ4)] |\n| MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients | Duke University | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FCao_MPAF_Model_Poisoning_Attacks_to_Federated_Learning_Based_on_Fake_CVPRW_2022_paper.html)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.08669)] [[SILDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002Fmpaf.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FH3fetWD_ZHw)] |\n| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FWen_Communication-Efficient_Federated_Data_Augmentation_on_Non-IID_Data_CVPRW_2022_paper.html)] |\n| Does Federated Dropout Actually Work? | Stanford | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FCheng_Does_Federated_Dropout_Actually_Work_CVPRW_2022_paper.html)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FiJ3Q_gNhXGE)] |\n| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [[PUB](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022W\u002FFedVision\u002Fhtml\u002FLuo_FedIris_Towards_More_Accurate_and_Privacy-Preserving_Iris_Recognition_via_Federated_CVPRW_2022_paper.html)] [[SLIDES](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fchenchen\u002FFedVision-Workshop-CVPR2022\u002Fpresentation-%20Zhengquan%20Luo.pdf)] [[VIDEO](https:\u002F\u002Fyoutu.be\u002FbRMeXncAjWY)] |\n| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Johns Hopkins University | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578476)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.02148)] [[CODE](https:\u002F\u002Fgithub.com\u002Fguopengf\u002FFL-MRCM)] |\n| Model-Contrastive Federated Learning :fire: | NUS; UC Berkeley | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578660)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16257)] [[CODE](https:\u002F\u002Fgithub.com\u002FQinbinLi\u002FMOON)] [[解读](https:\u002F\u002Fweisenhui.top\u002Fposts\u002F17666.html)] |\n| FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space :fire: | CUHK | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9577482)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06030)] [[CODE](https:\u002F\u002Fgithub.com\u002Fliuquande\u002FFedDG-ELCFS)] |\n| Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Duke University | CVPR | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9578192)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06043)] [[CODE](https:\u002F\u002Fgithub.com\u002Fjeremy313\u002FSoteria)] |\n| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710573)] |\n| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710586)] [[PDF](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGong_Ensemble_Attention_Distillation_for_Privacy-Preserving_Federated_Learning_ICCV_2021_paper.pdf)] |\n| Collaborative Unsupervised Visual Representation Learning from Decentralized Data | NTU; SenseTime | ICCV | 2021 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9710366)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06492)] |\n| Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification | NTU | MM | 2021 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3474085.3475182)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.06493)] |\n| Federated Visual Classification with Real-World Data Distribution | MIT; Google | ECCV | 2020 | [[PUB](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-58607-2_5)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.08082)] [[VIDEO](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Rc67rZzPDDY&ab_channel=TzuMingHsu)] |\n| InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages |  | MM | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394171.3413923)] |\n| Performance Optimization of Federated Person Re-identification via Benchmark Analysis **`data.`** | NTU | MM | 2020 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3394171.3413814)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.11560)] [[CODE](https:\u002F\u002Fgithub.com\u002Fcap-ntu\u002FFedReID)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F265987079)] |\n\n\u003C!-- 结束：联邦学习在顶级自然语言处理会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n\n## 联邦学习在顶级自然语言处理会议和期刊中\n\n被顶级人工智能和自然语言处理会议及期刊收录的联邦学习论文，包括 [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Facl\u002Findex.html)（计算语言学协会年会）、[NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fnaacl\u002Findex.html)（北美计算语言学协会分会）、[EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Femnlp\u002Findex.html)（自然语言处理中的经验方法会议）以及 [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fcoling\u002Findex.html)（计算语言学国际会议）。\n\n- [ACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AACL%3A) [2025](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2025\u002F)、[2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2024\u002F)、[2023](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2023\u002F)、[2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2022\u002F)、[2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2021\u002F)、[2019](https:\u002F\u002Faclanthology.org\u002Fevents\u002Facl-2019\u002F)\n- [NAACL](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANAACL-HLT%3A) [2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2024\u002F)、[2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2022\u002F)、[2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fnaacl-2021\u002F)\n- [EMNLP](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AEMNLP%3A) [2024](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2024\u002F)、[2023](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2023\u002F)、[2022](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2022\u002F)、[2021](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2021\u002F)、[2020](https:\u002F\u002Faclanthology.org\u002Fevents\u002Femnlp-2020\u002F)\n- [COLING](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ACOLING%3A) [2025](https:\u002F\u002Faclanthology.org\u002Fvolumes\u002F2025.coling-main\u002F)、[2020](https:\u002F\u002Faclanthology.org\u002Fevents\u002Fcoling-2020\u002F)\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级自然语言处理会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：联邦学习在顶级自然语言处理会议和期刊中 -->\n\n|标题                                                           |    机构                                          |    会议\u002F期刊             |    年份    |    文献|\n| ------------------------------------------------------------ | ------------------------------------------------- | -------------- | ---- | ------------------------------------------------------------ |\n| 面向异构客户端的鲁棒高效联邦低秩适应 |  | ACL | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.acl-long.19\u002F)] |\n| FedEx-LoRA：大型语言模型联邦高效微调的精确聚合 |  | ACL | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.acl-long.67\u002F)] |\n| 大型语言模型的联邦数据高效指令微调 |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.803\u002F)] |\n| FedDQC：大型语言模型联邦指令微调中的数据质量控制 |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.791\u002F)] |\n| 通信高效的张量化大型语言模型联邦微调 |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.1241\u002F)] |\n| FedLEKE：面向多客户端协作的联邦定位后编辑知识编辑 |  | ACL Findings | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.findings-acl.733\u002F)] |\n| 联邦学习中的梯度反演攻击：通过离散优化暴露文本数据。 |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.176\u002F)] |\n| FedMKT：大小语言模型之间的联邦互惠知识迁移。 |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.17\u002F)] |\n| 联邦增量式命名实体识别。 |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.13\u002F)] |\n| FedCSR：基于双对比学习的多平台跨域序列推荐联邦框架 |  | COLING | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-main.581\u002F)] |\n| 面向多产品问答的联邦检索增强生成 |  | COLING (Industry) | 2025 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2025.coling-industry.33\u002F)] |\n| 联邦学习系统中强差分隐私的无繁琐算法 |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-industry.64\u002F)] |\n| 安全地使用私有数据学习：面向大型语言模型的联邦学习框架 |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.303)] |\n| FEDKIM：自适应联邦知识注入医学基础模型 |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.464\u002F)] |\n| 基于费希尔信息的大型语言模型高效课程联邦学习 |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.587\u002F)] |\n| 异构LoRA用于设备端基础模型的联邦微调 |  | EMNLP | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.717\u002F)] |\n| 通过量化LoRA促进联邦学习中的数据与模型隐私 |  | EMNLP Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-emnlp.615\u002F)] |\n| 异构LoRA用于设备端基础模型的联邦微调 |  | EMNLP Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.717\u002F)] |\n| 基于公平选择的联邦学习，实现对低资源印度语言的通用多语言仇恨言论检测 |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.400\u002F)] |\n| 多模态原型驱动的开放词汇联邦学习 |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.314\u002F)] |\n| 按照攻击者意愿导航？面向联邦学习下鲁棒具身智能体的构建 |  | NAACL | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.57\u002F)] |\n| FedLFC：基于LoRA的语言家族聚类，迈向高效的联邦多语言建模。 |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.98\u002F)] |\n| 无梯度提示微调的文本分类个性化联邦学习。 |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.286\u002F)] |\n| 公共大型语言模型能否助力私有跨设备联邦学习？ |  | NAACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-naacl.59\u002F)] |\n| 带有偏见的视觉-语言模型下的公平联邦学习 |  | ACL Findings | 2024 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.595\u002F)] |\n| 参数高效的提示微调与自适应优化结合的大语言模型联邦学习 | 奥本大学 | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.488\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15080)] [[代码](https:\u002F\u002Fgithub.com\u002Fllm-eff\u002FFedPepTAO)] |\n| 情感与情绪感知的多模态投诉识别联邦元学习 | 帕特纳理工学院 | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.999\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002Fappy1608\u002FEMNLP2023-Multimodal-Complaint-Detection)] |\n| FedID：大规模预训练语言模型的联邦交互式蒸馏 | 横滨国立大学 | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.529\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002Fmaxinge8698\u002FFedID)] |\n| FedTherapist：通过联邦学习利用智能手机上用户生成的语言表达进行心理健康监测 | KAIST | EMNLP | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.734\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.16538)] |\n| 连续联邦学习的协同重放样本选择 | 卡内基梅隆大学 | EMNLP产业赛道 | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-industry.32\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.15054)] |\n| 可调软提示是联邦学习中的信使 | 中山大学 | EMNLP Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-emnlp.976\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.06805)] [[代码](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002Ffedsp\u002Ffederatedscope\u002Fnlp\u002Ffedsp)] |\n| 语义解析的联邦学习：任务定义、评估设置、新算法 | 俄亥俄州立大学 | ACL | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.678\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17221)] [[代码](https:\u002F\u002Fgithub.com\u002Fosu-nlp-group\u002Ffl4semanticparsing)] |\n| FEDLEGAL：首个面向法律NLP的真实世界联邦学习基准 | 哈尔滨工业大学；彭成实验室 | ACL | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.193\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedLegal)] |\n| 面向参数高效联邦学习的客户端定制化适配 |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.75\u002F)] |\n| 带有适配器的多语言神经机器翻译的通信高效联邦学习 |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.327\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12449)] [[代码](https:\u002F\u002Fgithub.com\u002Flancopku\u002Ffedmnmt)] |\n| 通过蒸馏结合异构标签集进行命名实体识别的联邦领域适应 |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.470\u002F)] |\n| FedPETuning：当联邦学习遇上预训练语言模型的参数高效微调方法 |  | ACL Findings | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.findings-acl.632\u002F)] |\n| Gboard语言模型的差分隐私联邦学习 |  | ACL Industry Track | 2023 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2023.acl-industry.60\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18465)] |\n| 联邦学习中的后门攻击：稀有嵌入与梯度集成 | 首尔国立大学 | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.6\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.14017)] |\n| 印度语推文表情符号预测的联邦方法 | 阿尔伯塔大学 | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.819\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.06401)] [[代码](https:\u002F\u002Fgithub.com\u002Fdeep1401\u002Ffedmoji)] |\n| 带有私有词汇表的文本分类联邦模型分解 | 哈尔滨工业大学；彭成实验室 | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.emnlp-main.430\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002FSMILELab-FL\u002FFedVocab)] |\n| 差分隐私文本编码器下的公平NLP模型 | 里尔大学 | EMNLP | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.514\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.06135)] [[代码](https:\u002F\u002Fgithub.com\u002Fsaist1993\u002Fdpnlp)] |\n| 通过选择性客户间转移实现文本分类的连续联邦学习 | DRIMCo GmbH；慕尼黑大学 | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.353\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06101)] [[代码](https:\u002F\u002Fgithub.com\u002Fraipranav\u002Ffcl-fedseit)] |\n| 基于隐私保护的关系嵌入聚合的高效知识图谱联邦学习 **`kg.`** | 利哈伊大学 | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.43\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09553)] [[代码](https:\u002F\u002Fgithub.com\u002Ftaokz\u002FFedR)] |\n| Dim-Krum：基于维度级Krum聚合的抗后门NLP联邦学习 | 北京大学 | EMNLP Findings | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.25\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06894)] |\n| 跨设备联邦学习中语言模型规模的扩展 | Google | ACL研讨会 | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.2\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.09715)] |\n| 面向可扩展高效联邦学习的内在梯度压缩 | 牛津大学 | ACL研讨会 | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.4\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.02656)] |\n| ActPerFL：主动个性化联邦学习 | Amazon | ACL研讨会 | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.fl4nlp-1.1\u002F)] [[网页](https:\u002F\u002Fwww.amazon.science\u002Fpublications\u002Factperfl-active-personalized-federated-learning)] |\n| FedNLP：自然语言处理任务的联邦学习方法基准测试 :fire: | 南加州大学 | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.findings-naacl.13\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.08815)] [[代码](https:\u002F\u002Fgithub.com\u002FFedML-AI\u002FFedNLP)] |\n| 带有噪声用户反馈的联邦学习 | 南加州大学；Amazon | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.196\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03092)] |\n| 通过联邦学习训练混合领域翻译模型 | Amazon | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.186\u002F)] [[网页](https:\u002F\u002Fwww.amazon.science\u002Fpublications\u002Ftraining-mixed-domain-translation-models-via-federated-learning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.01557)] |\n| 多语言联邦学习的预训练模型 | 约翰斯·霍普金斯大学 | NAACL | 2022 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.101\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.02291)] [[代码](https:\u002F\u002Fgithub.com\u002Forionw\u002Fmultilingual-federated-learning)] |\n| 带有全局字符关联的联邦中文分词 | 华盛顿大学 | ACL研讨会 | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.376\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002Fcuhksz-nlp\u002FGCASeg)] |\n| Efficient-FedRec：隐私保护新闻推荐的高效联邦学习框架 | 中国科学技术大学 | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.223\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.05446)] [[代码](https:\u002F\u002Fgithub.com\u002Fyjw1029\u002FEfficient-FedRec)] [[视频](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.223.mp4)] |\n| 通过主题记忆改进基于方面的情感分析联邦学习 | 香港中文大学（深圳） | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.321\u002F)] [[代码](https:\u002F\u002Fgithub.com\u002Fcuhksz-nlp\u002FASA-TM)] [[视频](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.321.mp4)] |\n| NLP的安全高效联邦学习框架 | 康涅狄格大学 | EMNLP | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.606\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.11934)] [[视频](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.606.mp4)] |\n| 联邦环境下的远程监督关系抽取 | 中国科学院大学 | EMNLP研讨会 | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.52\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.05049)] [[代码](https:\u002F\u002Fgithub.com\u002FDianboWork\u002FFedDS)] |\n| 带有噪声用户反馈的联邦学习 | 南加州大学；Amazon | NAACL研讨会 | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2022.naacl-main.196\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.03092)] |\n| 在联邦框架下对差分隐私序列标注的探索 | 汉堡大学 | NAACL研讨会 | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.privatenlp-1.4\u002F)] |\n| 理解联邦学习下语言模型中的意外记忆现象 | Google | NAACL研讨会 | 2021 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2021.privatenlp-1.1\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07490)] |\n| FedED：基于集成蒸馏的医疗关系抽取联邦学习 | 中国科学院 | EMNLP | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.165\u002F)] [[视频](https:\u002F\u002Fslideslive.com\u002F38939230)] [[解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F539347225)] |\n| 面向自然语言处理的机构级联邦学习实证研究 | 平安科技 | EMNLP研讨会 | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.55\u002F)] |\n| 口语理解的联邦学习 | 北京大学 | COLING | 2020 | [[PUB](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.310\u002F)] |\n| 两阶段联邦表型分析与患者表征学习 | 波士顿儿童医院哈佛医学院 | ACL研讨会 | 2019 | [[PUB](https:\u002F\u002Faclanthology.org\u002FW19-5030)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.05596)] [[代码](https:\u002F\u002Fgithub.com\u002Fkaiyuanmifen\u002FFederatedNLP)] [[UC.](https:\u002F\u002Fgithub.com\u002FMarcioPorto\u002Ffederated-phenotyping)] |\n\n\u003C!-- END:fl-in-top-nlp-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n\n\n## 联邦学习在顶级信息检索会议和期刊中的研究\n\n被顶级信息检索会议和期刊收录的联邦学习论文，包括 [SIGIR](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fsigir\u002Findex.html)（年度国际ACM SIGIR信息检索研究与发展大会）。\n\n- [SIGIR](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGIR%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3726302), [2024](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3626772), [2023](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3539618), [2022](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3477495), [2021](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3404835), [2020](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3397271)\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级信息检索会议和期刊中的研究\u003C\u002Fsummary>\n\n\u003C!-- START:fl-in-top-ir-conference-and-journal -->\n\n|标题                                                           |    机构                        |    会议\u002F期刊    |    年份    |    资料|\n| ------------------------------------------------------------ | ------------------------------- | ----- | ---- | ------------------------------------------------------------ |\n| FedCIA：用于隐私保护推荐的联邦协作式信息聚合 |  | SIGIR | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3729977)] |\n| NodeRec+：一种轻量级的联邦推荐系统框架 |  | SIGIR | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730138)] |\n| 针对联邦在线排序学习的遗忘机制：一项可复现性研究 |  | SIGIR | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730336)] |\n| 联邦推荐中的联合项目嵌入双视角探索与自适应局部-全局融合 |  | SIGIR | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3726302.3730016)] |\n| ReFer：面向全用户利益的增强型垂直联邦推荐 | 清华大学 | SIGIR | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657763)] |\n| 再探联邦推荐中的定向模型投毒攻击：通过多目标运输优化 | 浙江大学 | SIGIR | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657764)] |\n| FeB4RAG：在检索增强生成背景下评估联邦搜索 | 昆士兰大学 | SIGIR | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657853)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.11891)] [[代码](https:\u002F\u002Fgithub.com\u002Fielab\u002FFeB4RAG)] |\n| FedUD：利用非对齐数据进行跨平台联邦点击率预测 | 阿里巴巴集团 | SIGIR | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3626772.3657941)] |\n| 面向异构文本的个性化联邦关系分类 | 国防科技大学 | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591748)] |\n| 面向稀疏数据的细粒度偏好感知个性化联邦POI推荐 | 山东大学 | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591688)] |\n| 联邦推荐系统的操纵：利用合成用户进行投毒及其应对措施 | 昆士兰大学 | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591722)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03054)] |\n| FedAds：基于垂直联邦学习的隐私保护CVR预估基准 | 阿里巴巴集团 | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591909)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.08328)] [[代码](https:\u002F\u002Fgithub.com\u002Falibaba\u002FElastic-Federated-Learning-Solution\u002Ftree\u002FFedAds)] |\n| 边缘-云协同学习：结合联邦与集中式特征（短文） | 浙江大学 | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591976)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.05871)] |\n| FLIRT：面向信息检索的联邦学习（扩展摘要） | IMT Lucca | SIGIR | 2023 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591926)] |\n| 在联邦在线排序学习中，非独立同分布数据是否构成威胁？ | 昆士兰大学 | SIGIR | 2022 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3477495.3531709)] [[代码](https:\u002F\u002Fgithub.com\u002Fielab\u002F2022-SIGIR-noniid-foltr)] |\n| FedCT：用于推荐的联邦协作式迁移学习 | 罗格斯大学 | SIGIR | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462825)] [[PDF](http:\u002F\u002Fyongfeng.me\u002Fattach\u002Fliu-sigir2021.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002FCharlieMat\u002FEdgeCDR)] |\n| 关于联邦流水线的隐私问题 | 慕尼黑工业大学 | SIGIR | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462996)] |\n| FedCMR：联邦跨模态检索。 | 大连理工大学 | SIGIR | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3404835.3462989)] [[代码](https:\u002F\u002Fgithub.com\u002FhasakiXie123\u002FFedCMR)] |\n| 用于联邦评分预测的元矩阵分解。 | 山东大学 | SIGIR | 2020 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3397271.3401081)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.10086)] |\n\n\u003C!-- END:fl-in-top-ir-conference-and-journal -->\n\n\u003C\u002Fdetails>\n\n## 联邦学习在顶级数据库会议和期刊中的研究\n\n被顶级数据库会议和期刊收录的联邦学习论文，包括 [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fsigmod\u002Findex.html)（ACM SIGMOD大会）、[ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Ficde\u002Findex.html)（IEEE国际数据工程大会）以及 [VLDB](https:\u002F\u002Fdblp.uni-trier.de\u002Fdb\u002Fconf\u002Fvldb\u002Findex.html)（超大型数据库大会）。\n\n- [SIGMOD](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federated%20streamid%3Aconf%2Fsigmod%3A) [2022](https:\u002F\u002F2022.sigmod.org\u002Fsigmod_research_list.shtml), [2021](https:\u002F\u002F2021.sigmod.org\u002Fsigmod_research_list.shtml)\n- [ICDE](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AICDE%3A) [2025](https:\u002F\u002Fieee-icde.org\u002F2025\u002Fresearch-papers\u002F), [2024](https:\u002F\u002Ficde2024.github.io\u002F), [2023](https:\u002F\u002Ficde2023.ics.uci.edu\u002Fpapers-research-track\u002F), [2022](https:\u002F\u002Ficde2022.ieeecomputer.my\u002Faccepted-research-track\u002F), [2021](https:\u002F\u002Fieeexplore.ieee.org\u002Fxpl\u002Fconhome\u002F9458599\u002Fproceeding)\n- [VLDB](https:\u002F\u002Fdblp.org\u002Fsearch?q=federated%20streamid%3Ajournals%2Fpvldb%3A) [2025](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F18), [2024](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [2023](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvolumes\u002F17), [2022](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol16-volume-info\u002F), [2021](https:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol15-volume-info\u002F), [2021](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002F), [2020](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13-volume-info\u002F)\n\n\u003Cdetails open>\n\u003Csummary>联邦学习在顶级数据库会议和期刊中的研究\u003C\u002Fsummary>\n\u003C!-- START:fl-in-top-db-conference-and-journal -->\n\n|标题                                                        | 机构                     | 会议           | 年份 | 材料|\n| ------------------------------------------------------------ | ------------------------------- | --------------- | ---- | ------------------------------------------------------------ |\n| PS-MI：垂直联邦学习中的准确、高效且隐私保护的数据估值 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748215)] [[代码](https:\u002F\u002Fgithub.com\u002FZhouXiaokay\u002FVF-SV)] |\n| 基于缺失互补性的联邦不完全表格数据预测 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748213)] [[代码](https:\u002F\u002Fgithub.com\u002FLS5221\u002FDARN)] |\n| 高维数据的联邦平衡聚类 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3749646.3749673)] [[代码](https:\u002F\u002Fgithub.com\u002Fwhu-totemdb\u002FTeb-means)] |\n| FedVSE：面向联邦数据库的隐私保护且高效的向量搜索引擎 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3750601.3750674)] |\n| GORAM：用于联邦图上高效自我中心查询的图导向ORAM |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3748191.3748218)] [[代码](https:\u002F\u002Fgithub.com\u002FFannxy\u002FGORAM-ABY3)] |\n| 联邦数据分布偏移估计 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3742728.3742736)] [[代码](https:\u002F\u002Ffigshare.com\u002Fs\u002F7a1c725a293d1c5b88a8)] |\n| OpenFGL：联邦图学习的全面基准 |  | VLDB | 2025 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol18\u002Fp1305-li.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002FxkLi-Allen\u002FOpenFGL)] |\n| 垂直联邦学习中基于谈判的特征交易方法 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113094)] |\n| pFSSL-D：双阶段联邦半监督学习中的泛化与个性化 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113223)] |\n| FedEcover：具有高效覆盖子模型提取的快速稳定收敛的异构联邦学习 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113012)] |\n| 带有隐私保护聚类的联邦轨迹相似性学习 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113111)] |\n| FedSDP：用于个性化联邦学习的联邦自衍生原型 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112874)] |\n| 基于差分隐私密度估计模型的联邦数据分析。 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112917)] |\n| 联邦学习中的高效数据估值近似：一种基于采样的方法。 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112901)] |\n| pFedAFM：移动边缘设备上异构联邦学习中用于数据级个性化的自适应特征混合。 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113035)] |\n| 异构感知交通预测：一种隐私保护的联邦学习框架。 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113014)] |\n| 使用无人机在分布式未知数据上的在线联邦学习 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11112962)] |\n| 追踪数据多样性：迈向垂直联邦学习中的参与者选择。 |  | ICDE | 2025 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11113194)] |\n| FedMix：通过数据混合提升垂直联邦学习效果 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597834)] |\n| FedCross：通过多模型交叉聚合实现精准联邦学习 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597740)] |\n| 客户帮助客户：半监督联邦学习中的交替协作 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598007)] |\n| 半异步在线联邦众包 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598143)] |\n| AdaFGL：一种针对拓扑异质性的联邦节点分类新范式 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597891)] |\n| MergeSFL：带有特征合并和批量大小调节的分裂联邦学习 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597905)] |\n| LightTR：一个轻量级的联邦轨迹恢复框架 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598171)] |\n| Feed：迈向个性化有效的联邦学习 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597724)] |\n| 联邦学习中的标签噪声校正：一种安全、高效且可靠的实现 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597841)] |\n| 快速、鲁棒且可解释的联邦学习参与者贡献评估 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597940)] |\n| HeteFedRec：具有模型异质性的联邦推荐系统 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598074)] |\n| 隐藏你的模型：一种无需参数传输的联邦推荐系统 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597708)] |\n| FedCTQ：一个基于联邦的精准高效接触追踪查询框架 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10598064)] |\n| 防止基于热门物品嵌入的攻击在联邦推荐中发生 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597721)] |\n| RobFL：通过特征中心分离和恶意中心检测实现的鲁棒联邦学习 |  | ICDE | 2024 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10597878)] |\n| 在超边缘端对LLM进行联邦微调：好的、坏的、丑陋的 | TUM | DEEM@SIGMOD | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3650203.3663331)] |\n| FedSQ：一个用于联邦向量相似度查询的安全系统 |  | VLDB | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685895)] |\n| FedSM：一个实用的联邦共享出行系统 |  | VLDB | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685896)] |\n| OFL-W3：一个基于Web 3.0的一次性联邦学习系统 |  | VLDB | 2024 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3685800.3685900)] |\n| 联邦学习中的贡献评估：一项全面的实验评估 |  | VLDB | 2024 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp2077-li.pdf)] |\n| Uldp-FL：跨孤岛用户级差分隐私的联邦学习。 |  | VLDB | 2024 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp2826-kato.pdf)] |\n| 基于拍卖的联邦学习性能定价 | 阿里集团 | VLDB | 2024 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FPerformance-Based%20Pricing%20of%20Federated%20Learning%20via%20Auction)] [[代码](https:\u002F\u002Fgithub.com\u002FZiTao-Li\u002Ffl_auction)] |\n| 用于集群式联邦学习的区块链系统，支持点对点知识转移 | NJU | VLDB | 2024 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FA%20Blockchain%20System%20for%20Clustered%20Federated%20Learning%20with%20Peer-to-Peer%20Knowledge%20Transfer)] [[代码](https:\u002F\u002Fgithub.com\u002Fnju-websoft\u002FFedChain)] |\n| 通信高效且可证明的联邦去学习 | SDU；KAUST | VLDB | 2024 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvolumes\u002F17\u002Fpaper\u002FCommunication%20Efficient%20and%20Provable%20Federated%20Unlearning)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.11018)] [[代码](https:\u002F\u002Fgithub.com\u002FHappy2Git\u002FFATS_supplement)] |\n| 提升非独立同分布数据在异构设备上的去中心化联邦学习 | USTC | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184749)] |\n| 异构图上联邦学习的客户端和参数动态激活 | 哥伦比亚大学 | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184557)] [[代码](https:\u002F\u002Fgithub.com\u002Fdongzizhu\u002FFedDA)] |\n| FedKNOW：边缘端融合标志性任务知识的联邦持续学习 | BIT | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184531)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01738)] |\n| Lumos：面向去中心化设备的异构感知联邦图学习 | SJTU | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184796)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.00492)] |\n| 联邦物联网交互漏洞分析 | MSU | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184681)] |\n| 非独立同分布数据上的分布规整联邦学习 | BUAA | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184650)] |\n| Fed-SC：高维数据上的一次性联邦子空间聚类 | 上海科技大学 | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184550)] [[代码](https:\u002F\u002Fgithub.com\u002FSongjieXie\u002FFed-SC)] |\n| FLBooster：一个统一高效的联邦学习加速平台 | ZJU | ICDE | 2023 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10184883)] |\n| FedGTA：面向联邦图学习的拓扑感知平均 | BIT | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol17\u002Fp41-li.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002FxkLi-Allen\u002FFedGTA)] |\n| FS-Real：一个真实的跨设备联邦学习平台。 | 阿里集团 | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp4046-chen.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13363)] [[代码](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope\u002Ftree\u002FFSreal)] |\n| 二分类器的联邦校准与评估。 | meta | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp3253-cormode.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.12526)] [[代码](https:\u002F\u002Ffigshare.com\u002Fs\u002F607998e479b0778645f6)] |\n| Olive：在可信执行环境中进行的无感知联邦学习，以应对稀疏化风险。 | 京都大学 | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp2404-kato.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.07165)] [[代码](https:\u002F\u002Fgithub.com\u002FFumiyukiKato\u002FFL-TEE)] |\n| Falcon：一个隐私保护且可解释的垂直联邦学习系统。 | NUS | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp2471-ooi.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002Fnusdbsystem\u002Ffalcon)] |\n| 差分隐私下的垂直联邦聚类。 | 普渡大学 | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1277-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01700)] [[代码](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002Fpublic_vflclustering-63CD\u002FREADME.md)] |\n| FederatedScope：一个灵活的联邦学习平台，适用于异构环境。 :fire: | 阿里 | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1059-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.05011)] [[代码](https:\u002F\u002Fgithub.com\u002Falibaba\u002FFederatedScope)] |\n| 跨孤岛联邦学习的安全夏普利值。 | 京都大学 | VLDB | 2023 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp1657-zheng.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04856)] [[代码](https:\u002F\u002Fgithub.com\u002Fteijyogen\u002Fsecsv)] |\n| OpBoost：一个基于保序脱敏的垂直联邦树增强框架 | ZJU | VLDB | 2022 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol16\u002Fp202-li.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.01318)] [[代码](https:\u002F\u002Fgithub.com\u002Falibaba-edu\u002Fmpc4j\u002Ftree\u002Fmain\u002Fmpc4j-sml-opboost)] |\n| Skellam混合机制：一种新颖的差分隐私联邦学习方法。 | NUS | VLDB | 2022 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp2348-bao.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002FSkellamMixtureMechanism\u002FSMM)] |\n| 通过缓存支持的本地更新实现通信高效的垂直联邦学习训练 | PKU | VLDB | 2022 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3547305.3547316)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.14628)] [[代码](https:\u002F\u002Fgithub.com\u002Fccchengff\u002FFDL\u002Ftree\u002Fmain\u002Fplayground\u002Fcelu_vfl)] |\n| FedTSC：一个用于可解释时间序列分类的安全联邦学习系统。 | HIT | VLDB | 2022 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp3686-wang.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002Fhit-mdc\u002FFedTSC-FedST)] |\n| 提高水平联邦学习中数据估值的公平性 | UBC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835382)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09046)] |\n| FedADMM：一个鲁棒的联邦深度学习框架，能够适应系统异质性 | USTC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835545)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.03529)] [[代码](https:\u002F\u002Fgithub.com\u002FYonghaiGong\u002FFedADMM)] |\n| FedMP：通过异构边缘计算中的自适应模型剪枝进行联邦学习。 | USTC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835327)] |\n| 非独立同分布数据孤岛上的联邦学习：一项实验研究。 :fire: | NUS | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835537)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.02079)] [[代码](https:\u002F\u002Fgithub.com\u002FXtra-Computing\u002FNIID-Bench)] |\n| 通过异构边缘计算中的智能模型迁移提升联邦学习 | USTC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835657)] |\n| Samba：一个用于安全联邦多臂老虎机的系统 | 克莱蒙特奥弗涅大学 | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835585)] [[代码](https:\u002F\u002Fgithub.com\u002Fgamarcad\u002Fsamba-demo)] |\n| FedRecAttack：针对联邦推荐的模型中毒攻击 | ZJU | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835228)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.01499)] [[代码](https:\u002F\u002Fgithub.com\u002Frdz98\u002Ffedrecattack)] |\n| 通过云端未标注数据提升联邦学习 | USTC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835163)] |\n| 高效评估水平和垂直联邦学习的参与者贡献 | USTC | ICDE | 2022 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9835159)] |\n| 联邦计算入门 | 沃里克大学；Facebook | SIGMOD教程 | 2022 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3514221.3522561)] |\n| BlindFL：一种不窥探你数据的垂直联邦机器学习 | PKU；腾讯 | SIGMOD | 2022 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3514221.3526127)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07975)] |\n| 一种跨孤岛联邦排序学习的有效方法 | BUAA | ICDE | 2021 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458704)] [[相关论文(中文)](https:\u002F\u002Fkns.cnki.net\u002Fkcms\u002Fdetail\u002Fdetail.aspx?doi=10.13328\u002Fj.cnki.jos.006174)] |\n| 垂直联邦学习中针对模型预测的特征推断攻击 | NUS | ICDE | 2021 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458672\u002F)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.10152)] [[代码](https:\u002F\u002Fgithub.com\u002Fxj231\u002Ffeatureinference-vfl)] |\n| 高效联邦学习模型调试 | USTC | ICDE | 2021 | [[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9458829)] |\n| 带有隐私保障的联邦矩阵分解 | 普渡 | VLDB | 2021 | [[论文](https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp900-li.pdf)] |\n| 投影式联邦平均与异构差分隐私。 | 中国人民大学 | VLDB | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.14778\u002F3503585.3503592)] [[代码](https:\u002F\u002Fgithub.com\u002FEmory-AIMS\u002FPFA)] |\n| 为联邦学习启用基于SQL的数据训练调试 | 西蒙弗雷泽大学 | VLDB | 2021 | [[论文](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol15\u002Fp388-wu.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.11884)] [[代码](https:\u002F\u002Fgithub.com\u002Fsfu-db\u002FFedRain-and-Frog)] |\n| Refiner：一个由区块链驱动的可靠激励型联邦学习系统 | ZJU | VLDB | 2021 | [[论文](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol14\u002Fp2659-jiang.pdf)] |\n| Tanium Reveal：一个用于大型企业网络上查询非结构化文件数据的联邦搜索引擎 | Tanium Inc. | VLDB | 2021 | [[论文](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol14\u002Fp3096-stoddard.pdf)] [[视频](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Wg411j7aA)] |\n| VF2Boost：非常快速的垂直联邦梯度提升，用于跨企业学习 | PKU | SIGMOD | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3448016.3457241)] |\n| ExDRa：基于联邦原始数据的探索性数据科学 | SIEMENS | SIGMOD | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3448016.3457549)] |\n| 在恶劣边缘计算环境下的区块链与联邦学习联合卸载 | TJU | SIGMOD研讨会 | 2021 | [[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3460866.3461765)] |\n| 针对树模型的隐私保护垂直联邦学习 | NUS | VLDB | 2020 | [[论文](http:\u002F\u002Fvldb.org\u002Fpvldb\u002Fvol13\u002Fp2090-wu.pdf)] [[PDF](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06170)] [[视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sjii8oVCqiY)] [[代码](https:\u002F\u002Fgithub.com\u002Fnusdbsystem\u002Fpivot)] |\n\n\u003C!-- 结束：fl-在顶级网络会议和期刊中 -->\n\n\u003C\u002Fdetails>\n\n\n\n## fl 在顶级网络会议和期刊中\n\n\n联邦学习论文被顶级数据库会议和期刊录用，包括 [SIGCOMM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fsigcomm\u002Findex.html)（计算机通信应用、技术、架构与协议大会）、[INFOCOM](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Finfocom\u002Findex.html)（IEEE计算机通信大会）、[MobiCom](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fmobicom\u002Findex.html)（ACM\u002FIEEE移动计算与网络国际会议）、[NSDI](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fnsdi\u002Findex.html)（网络系统设计与实现研讨会）以及 [WWW](https:\u002F\u002Fdblp.org\u002Fdb\u002Fconf\u002Fwww\u002Findex.html)（万维网大会）。\n\n- [SIGCOMM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ASIGCOMM%3A) 2025\n- [INFOCOM](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AINFOCOM%3A) [2025](https:\u002F\u002Finfocom2025.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2024](https:\u002F\u002Finfocom2024.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2023](https:\u002F\u002Finfocom2023.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference), [2022](https:\u002F\u002Finfocom2022.ieee-infocom.org\u002Fprogram\u002Faccepted-paper-list-main-conference)([页面](https:\u002F\u002Finfocom.info\u002Fday\u002F3\u002Ftrack\u002FTrack%20B#B-7)), [2021](https:\u002F\u002Finfocom2021.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html)([页面](https:\u002F\u002Fduetone.org\u002Finfocom21)), [2020](https:\u002F\u002Finfocom2020.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html)([页面](https:\u002F\u002Fduetone.org\u002Finfocom20)), [2019](https:\u002F\u002Finfocom2019.ieee-infocom.org\u002Faccepted-paper-list-main-conference.html), 2018\n- [MobiCom](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AMobiCom%3A) [2024](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2024\u002Faccepted.html), [2023](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2023\u002Faccepted.html), [2022](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2022\u002Faccepted.html), [2021](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2021\u002Faccepted.html), [2020](https:\u002F\u002Fwww.sigmobile.org\u002Fmobicom\u002F2020\u002Faccepted.php)\n- [NSDI](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3ANSDI%3A) [2025](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Ftechnical-sessions), 2023([春季](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Fspring-accepted-papers), [秋季](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi23\u002Ffall-accepted-papers))\n- [WWW](https:\u002F\u002Fdblp.uni-trier.de\u002Fsearch?q=federate%20venue%3AWWW%3A) [2025](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fproceedings\u002F10.1145\u002F3696410), [2024](https:\u002F\u002Fwww2024.thewebconf.org\u002Faccepted\u002Fresearch-tracks\u002F), [2023](https:\u002F\u002Fwww2023.thewebconf.org\u002Fprogram\u002Faccepted-papers\u002F), [2022](https:\u002F\u002Fwww2022.thewebconf.org\u002Faccepted-papers\u002F), [2021](https:\u002F\u002Fwww2021.thewebconf.org\u002Fprogram\u002Fpapers-program\u002Flinks\u002Findex.html)\n\n\u003Cdetails open>\n\u003Csummary>fl 在顶级网络会议和期刊中\u003C\u002Fsummary>\n\u003C!-- 开始：fl-在顶级网络会议和期刊中 -->\n\n|标题                                                           |    所属机构                                                     |    会议\u002F期刊         |    年份    |    文献|\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | ---- | ------------------------------------------------------------ |\n| 一种面向能耗感知的联邦学习轻量级仿真框架 |  | SIGCOMM（海报与演示） | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748395)] |\n| NEBULA——面向异构网络的去中心化联邦学习 |  | SIGCOMM（海报与演示） | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748413)] |\n| 联邦推理：迈向边缘设备上的协作式隐私保护推理 |  | SIGCOMM（海报与演示） | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3744969.3748418)] |\n| 针对图数据上垂直联邦学习的偏好画像攻击 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044459)] |\n| FedGPA：用于边缘异常检测的全局-个性化协同联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044589)] |\n| 基于异构量化和LoRA的大语言模型联邦自适应微调 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044641)] |\n| FLM-TopK：通过稀疏化区间梯度加速联邦大语言模型调优 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044514)] |\n| 面向通信高效分布式极小极大优化的客户端采样 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044637)] |\n| 面向不可靠网络系统的鲁棒上下文组合多臂老虎机问题 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044618)] |\n| ElasticFed：面向边缘联邦持续学习的大小模型协同训练 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044503)] |\n| γ-FedHT：联邦学习中的步长感知硬阈值梯度压缩 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044558)] |\n| PSFL：具有收敛性保证的并行-串行联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044534)] |\n| GraphRx：面向上行神经接收机的多小区基于图的协作学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044726)] |\n| 输入完整性和结果真实性：迈向联邦学习中的可信聚合 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044719)] |\n| 具有差分隐私和容错能力的轻量级联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044562)] |\n| 通信高效的异步随机梯度下降 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044686)] |\n| GeoFL：高效地理分布跨设备联邦学习框架 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044713)] |\n| FedPDA：用于降低神经接收机在线适应频率的协作学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044747)] |\n| LCO-AGQ：面向联邦学习的轻量级客户端导向自适应梯度量化算法 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044636)] |\n| FedEXT：具有边缘模型互补扩展的差异化联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044645)] |\n| FedFetch：通过自适应下游预取加速联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044717)] |\n| 基于相似性引导的异构边缘计算上联邦智能快速部署 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044586)] |\n| CARE：面向预算有限请求方的兼容性感知联邦学习激励机制 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044535)] |\n| 面向延迟信息的无线在线联邦学习的受限空中模型更新 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044570)] |\n| 面向数据中心协作式网络优化的多任务强化学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044699)] |\n| 走向联邦推理：面向协作式边缘AI的在线模型集成框架 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044578)] |\n| VaniKG：针对鲁棒联邦聚合的消失关键梯度攻击与防御 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044620)] |\n| 利用量子密钥分发对抗跨silos联邦学习中的梯度深度泄漏 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044743)] |\n| FedUFD：利用联邦不确定性驱动的特征蒸馏实现个性化边缘计算 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044664)] |\n| 利用可迁移图神经网络加速动态D2D网络中的聚类联邦学习 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044690)] |\n| 面向流式数据的AoI感知联邦遗忘：结合在线客户端选择与定价 |  | INFOCOM | 2025 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11044760)] |\n| 动态图遗忘：一种通用且高效的梯度变换后处理方法 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714911)] |\n| 利用潜在环境赋能联邦图理性学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714929)] |\n| Aegis：面向联邦推荐系统、抵御属性推断攻击的训练后属性遗忘 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714823)] |\n| P4GCN：具有隐私保护的双端图卷积网络的垂直联邦社交推荐 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714721)] |\n| 具有时序相关性的无限数据流局部差分隐私发布 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714619)] |\n| 在隐私风险下，遗忘激励学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714740)] |\n| Maverick：基于对比学习的个性化边缘辅助联邦学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714884)] |\n| 水平联邦异构图学习：应对数据分布挑战的多尺度自适应方案 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714722)] |\n| 处理联邦学习中的噪声数据：一种灵活定价的激励机制 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714961)] |\n| 大型（视觉）语言模型中基于自我比较的数据集成员身份推断 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714703)] |\n| 基于解耦表征学习的联邦图异常检测 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714567)] |\n| FedMobile：支持不完全模态的知识贡献感知多模态联邦学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714623)] |\n| 子图联邦遗忘 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714821)] |\n| 基于自适应知识融合的冷启动用户个性化联邦推荐 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714635)] |\n| NI-GDBA：基于联邦图学习中自适应扰动的非侵入式分布式后门攻击 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714630)] |\n| FedRIR：重新思考联邦学习中的信息表示 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3696410.3714612)] |\n| PM-MOE：面向个性化联邦学习的私有模型参数专家混合 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714561)] |\n| 可证明鲁棒的联邦强化学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714728)] |\n| MoCFL：面向高度动态网络的移动集群联邦学习框架 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714515)] |\n| Flock：基于实用区块链状态通道的鲁棒且隐私保护的联邦学习 |  | WWW | 2025 | [[PUB](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3696410.3714666)] |\n| PAPAYA联邦分析栈：兼顾隐私、可扩展性和实用性 |  | NSDI | 2025 | [[PUB](https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Fnsdi25\u002Fpresentation\u002Fsrinivas)] [[SLIDE](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fnsdi25_slides-srinivas.pdf)] |\n| 破解安全聚合：联邦学习中聚合梯度导致的标签泄露 |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621090)] |\n| 联邦遗忘中的策略性数据撤销 |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621201)] |\n| FedTC：通过变换编码实现通信高效的联邦学习 |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621176)] |\n| 在提供模型即服务的同时进行联邦学习：联合训练与推理优化 |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621105)] |\n| FairFed：通过合作式Shapley值提升联邦学习中贡献评估的公平性和效率 |  | INFOCOM | 2024 | [[PUB](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F10621438)] |\n| DPBalance：面向模型即服务的联邦学习的高效公平隐私预算调度 |  | INFOCOM | 2024 | [[PUB](https:","# Awesome-FL 快速上手指南\n\n**项目说明**：\nAwesome-FL 并非一个可直接安装运行的软件框架或库，而是一个**联邦学习（Federated Learning）领域的资源汇总清单**。它收录了顶级会议\u002F期刊论文、开源框架、数据集、综述文章及教程等核心资源。本指南将指导开发者如何高效利用该仓库获取所需资源。\n\n## 环境准备\n\n由于本项目主要为文档和资源链接集合，无需特定的系统环境或复杂的依赖安装。只需满足以下条件即可：\n\n*   **操作系统**：Windows, macOS, 或 Linux 均可。\n*   **必备工具**：\n    *   Web 浏览器（推荐 Chrome, Edge 或 Firefox）。\n    *   Git（用于克隆仓库到本地，可选）。\n    *   GitHub 账号（用于提交 Issue 或 PR 贡献资源，可选）。\n*   **网络建议**：\n    *   访问 GitHub 可能需要稳定的网络连接。\n    *   部分论文链接指向 dblp、ACM DL 或 IEEE Xplore，国内访问可能较慢，建议使用学术加速工具或机构网络。\n\n## 获取资源步骤\n\n你可以通过在线浏览或克隆仓库两种方式获取资源。\n\n### 方式一：在线浏览（推荐）\n直接访问项目维护的官方主页，体验更佳的阅读效果：\n```bash\n# 在浏览器中打开以下地址\nhttps:\u002F\u002Fyoungfish42.github.io\u002FAwesome-FL\n```\n\n### 方式二：克隆到本地\n如果你希望离线查看或通过代码管理资源，可以使用 Git 克隆：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL.git\ncd Awesome-FL\n```\n\n*注：国内用户若克隆速度慢，可使用镜像源（如 Gitee 镜像，如有）或配置 Git 代理。*\n\n## 基本使用\n\n本项目的核心用法是根据你的研究或开发需求，在目录中查找对应的资源链接。\n\n### 1. 查找特定领域的论文\n根据 `Table of Contents` 中的分类，定位到你关注的领域。例如，如果你关注**计算机视觉（CV）**方向的联邦学习：\n*   在页面中找到 `FL in top-tier conference and journal by category` 部分。\n*   点击 `[CV](#fl-in-top-cv-conference-and-journal)` 链接。\n*   查阅列出的 ICCV, CVPR, ECCV 等顶会论文列表及对应年份的接收论文链接。\n\n### 2. 寻找开源框架与数据集\n*   **框架开发**：点击侧边栏或目录中的 `[Framework](#framework)` 章节，获取当前主流的联邦学习开源框架列表（如 FATE, PaddleFL, Flower 等，具体以仓库最新内容为准）。\n*   **数据验证**：点击 `[Datasets](#datasets)` 章节，获取适用于联邦学习场景的标准数据集。\n\n### 3. 追踪最新进展\n*   查看 `Update log` 了解最新收录的资源。\n*   访问项目中提到的自动追踪工具：[FL-paper-update-tracker](https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FFL-paper-update-tracker) 以获取最新的论文动态。\n\n### 4. 参与贡献\n如果你发现了新的优质资源，可以通过以下方式贡献：\n*   **提交 Issue**：在 GitHub 仓库页面点击 `Issues` -> `New issue`，建议添加的资源。\n*   **提交 PR**：Fork 仓库后，修改 `README.md` 添加资源链接，并提交 Pull Request。\n*   **社区交流**：加入项目提到的联邦学习交流群（QQ 群号：833638275）进行讨论。\n\n> **注意**：根据仓库最新公告（2024\u002F09\u002F30），原作者已完成博士学业并调整研究方向，论文列表更新频率将调整为月度或季度。欢迎社区开发者共同维护此资源库。","某医疗 AI 初创团队正研发跨医院联合诊断模型，需在严格保护患者隐私的前提下，快速调研联邦学习在医学影像领域的最新算法与开源框架。\n\n### 没有 Awesome-FL 时\n- **文献检索如大海捞针**：研究人员需在 IEEE、CVPR 等多个顶级会议网站手动筛选，难以区分哪些论文真正涉及“联邦学习 + 医疗影像”，耗时数周仍可能遗漏关键成果。\n- **技术选型缺乏对比依据**：面对分散在各处的开源框架，团队无法快速评估哪个支持异构数据或具备差分隐私功能，导致原型开发阶段反复试错。\n- **数据与教程零散难寻**：适合联邦学习的医疗数据集隐藏在不同机构的个人页中，且缺乏配套的复现教程，新人上手门槛极高。\n- **前沿动态滞后**：由于缺乏统一的追踪机制，团队往往在项目中期才发现已有更优的聚合算法发表，导致技术路线被迫重构。\n\n### 使用 Awesome-FL 后\n- **精准定位顶会成果**：直接通过\"CV\"和\"FL in top-tier conference\"分类，一键获取近年所有关于联邦学习在计算机视觉（含医学影像）的核心论文，调研效率提升十倍。\n- **框架决策有的放矢**：利用\"Framework\"板块中按功能分类的列表，迅速锁定支持非独立同分布（Non-IID）数据的成熟框架，大幅缩短技术验证周期。\n- **资源一站式获取**：从\"Datasets\"和\"Tutorials\"栏目直接下载经过清洗的基准数据集及配套代码，团队成员当天即可跑通基线模型。\n- **紧跟学术脉搏**：借助其自动追踪机制和定期更新的日志，实时掌握最新的研讨会（Workshops）和特刊信息，确保技术方案始终处于行业前沿。\n\nAwesome-FL 将原本碎片化的学术资源整合为结构化知识图谱，让研发团队从繁琐的信息搜集者转变为专注技术创新的实践者。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fyoungfish42_Awesome-FL_2e631e3e.png","youngfish42","Yuwen Yang","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fyoungfish42_d9a7130b.jpg","Learned people know they can never learn enough.","Shanghai Jiao Tong University (SJTU)"," Shanghai, China","im.young@foxmail.com",null,"https:\u002F\u002Fwww.zhihu.com\u002Fpeople\u002Fyoungfish42\u002Factivities","https:\u002F\u002Fgithub.com\u002Fyoungfish42",[84,88],{"name":85,"color":86,"percentage":87},"Python","#3572A5",94.3,{"name":89,"color":90,"percentage":91},"HTML","#e34c26",5.7,1975,219,"2026-04-12T07:42:13","CC-BY-SA-4.0",1,"","未说明",{"notes":100,"python":98,"dependencies":101},"Awesome-FL 是一个联邦学习（Federated Learning）领域的资源列表（Awesome List），主要收录论文、框架、数据集、综述和教程等链接，本身不是一个可执行的软件工具或代码库，因此没有具体的操作系统、GPU、内存、Python 版本或依赖库的安装需求。用户需根据列表中引用的具体框架或论文代码去查看相应的环境要求。",[],[103,35,15,14,16],"其他",[105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"awesome","deep-learning","federated-learning","graph-neural-networks","machine-learning","tabular-data","federated-learning-framework","computer-vision","knowledge-graph","paper","security","natural-language-processing","information-retrieval","data-mining","database","system","artificial-intelligence","efficiency","privacy","graph",4,"2026-03-27T02:49:30.150509","2026-04-13T17:00:35.171654",[129,134,139,144],{"id":130,"question_zh":131,"answer_zh":132,"source_url":133},9346,"如何向该仓库推荐新的联邦学习框架、论文或综述？","用户可以通过创建 Issue 提供具体的资源名称和链接（如 arXiv 链接或项目官网）。维护者确认后会将其添加到列表中。例如，用户曾成功推荐了 FedSim 模拟器及多篇个性化联邦学习综述。","https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues\u002F23",{"id":135,"question_zh":136,"answer_zh":137,"source_url":138},9343,"为什么某些论文（如 CVPR 研讨会论文）没有被收录到列表中？","维护者会检查会议官网和录用论文列表，仅收录与联邦学习（Federated Learning）直接相关的文章。如果未找到相关文章，则不会收录。欢迎用户直接提交 Pull Request 贡献具体的论文链接和建议。","https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues\u002F35",{"id":140,"question_zh":141,"answer_zh":142,"source_url":143},9344,"如何判断一篇论文是否属于联邦学习范畴并被收录？","该仓库对联邦学习相关材料的收录标准相对宽松。只要论文在引言或方法中提到可应用于“联邦设置（federated settings）”或解决联邦学习相关问题（例如无需同时访问多个数据集），即使主要任务不是 FL，也可能被收录。","https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues\u002F28",{"id":145,"question_zh":146,"answer_zh":147,"source_url":148},9345,"列表中某个项目（如 FedGraph）的代码链接失效了怎么办？","这通常是因为论文作者暂时撤下了开源代码。维护者会在与作者沟通确认有新地址后更新链接。在此期间，建议直接联系论文作者获取代码或关注其主页更新。","https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fissues\u002F19",[150,155,160,165,170,175],{"id":151,"version":152,"summary_zh":153,"released_at":154},238283,"v1.0-alpha","The owner and principal contributor [@youngfish42](https:\u002F\u002Fgithub.com\u002Fyoungfish42) has successfully completed his doctoral studies 🎓 as of September 30.","2024-12-30T03:42:37",{"id":156,"version":157,"summary_zh":158,"released_at":159},238284,"v0.9.9-alpha","## What's Changed\r\n* Suggest to add new survey about privacy computing including FL to… by @6lyc in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F29\r\n* Add GOLF to Framework section by @MatZaharia in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F32\r\n* Fix flower domain by @Moep90 in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F34\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fcompare\u002Fv0.9.8-alpha...v0.9.9-alpha","2024-09-30T10:48:30",{"id":161,"version":162,"summary_zh":163,"released_at":164},238285,"v0.9.8-alpha","We restructured the entire project to give it a better presentation. We have added more conference and journal information and updated it to the December 2023 version.\r\n\r\n## What's Changed\r\n* build: remove tldr; optimize page rendering by @beiyuouo in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F25\r\n* update FedGCN and Fedrule, add Wyze Rule by @yh-yao in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F26\r\n* Update Readme paper CCS 2023 by @Armandolando in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F27\r\n\r\n## New Contributors\r\n* @Armandolando made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F27\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fcompare\u002Fv0.9.6-alpha...v0.9.8-alpha","2023-12-31T13:35:40",{"id":166,"version":167,"summary_zh":168,"released_at":169},238286,"v0.9.6-alpha","We have compiled information on top federated learning conferences and journals through June 2023, and a list of research papers on federated learning on graph and tabular data. 😄\r\n\r\n## What's Changed\r\n* Update INFOCOM 2023 papers by @Sprinter1999 in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F21\r\n* Update README.md by @JinheonBaek in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F22\r\n\r\n## New Contributors\r\n* @JinheonBaek made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F22\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fcompare\u002Fv0.8.0-alpha...v0.9.6-alpha","2023-06-06T11:04:20",{"id":171,"version":172,"summary_zh":173,"released_at":174},238287,"v0.9.0-alpha","For more convenient reading (easy jumping between citations and quotes, enhanced display on low resolution screens) we have added Github Pages feature.\r\n\r\nCheck out the [project homepage](https:\u002F\u002Fyoungfish42.github.io\u002FAwesome-Federated-Learning-on-Graph-and-Tabular-Data) at https:\u002F\u002Fyoungfish42.github.io\u002FAwesome-Federated-Learning-on-Graph-and-Tabular-Data for a better reading experience. 😃\r\n\r\n## What's Changed\r\n* add top secure conferences （just for test） by @Shmily1368 in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F1\r\n* update FL for NLP in ACL NAACL EMNLP by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F2\r\n* Update conference url  by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F3\r\n* add FL related papers from NDSS 2022 and USENIX 2022 by Shmily1368 ——version2 by @Shmily1368 in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F5\r\n* 添加s&p2023 by @Li-Hongcheng in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F7\r\n* Add Heter-aware by @AlvinIsonomia in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F8\r\n* Update FedGCN by @yh-yao in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F9\r\n* Update README.md by @xbfu in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F11\r\n* Add survey and course of MPC and FL. by @lokinko in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F12\r\n* feat(add): ci&cd ghpages by @beiyuouo in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F13\r\n* Sprinter1999 xuefen by @beiyuouo in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F15\r\n* INFOCOM2022 by @Li-Hongcheng in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F16\r\n* update nlp papers introduction by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F18\r\n\r\n## New Contributors\r\n* @Shmily1368 made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F1\r\n* @tenderzada made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F2\r\n* @Li-Hongcheng made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F7\r\n* @AlvinIsonomia made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F8\r\n* @yh-yao made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F9\r\n* @xbfu made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F11\r\n* @lokinko made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F12\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fcommits\u002Fv0.9.0-alpha","2022-09-22T06:42:33",{"id":176,"version":177,"summary_zh":178,"released_at":179},238288,"v0.8.0-alpha","The archived version before adding the Github Pages feature, at which time the basic information of 600+ papers (including federated learning on graph data and tabular data and federated learning papers in various top journals and top conferences) has been collated and verified (including source code, arxiv preprint hyperlinks and publication hyperlinks).\r\n\r\n## What's Changed\r\n* update FL for NLP in ACL NAACL EMNLP by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F2\r\n* Update conference url  by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F3\r\n* add FL related papers from NDSS 2022 and USENIX 2022 by Shmily1368 ——version2 by @Shmily1368 in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F5\r\n* 添加s&p2023 by @Li-Hongcheng in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F7\r\n* Add Heter-aware by @AlvinIsonomia in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F8\r\n* Update FedGCN by @yh-yao in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F9\r\n* Add survey and course of MPC and FL. by @lokinko in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F12\r\n* feat(add): ci&cd ghpages by @beiyuouo in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F13\r\n* Sprinter1999 xuefen by @beiyuouo in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F15\r\n* INFOCOM2022 by @Li-Hongcheng in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F16\r\n* update nlp papers introduction by @tenderzada in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F18\r\n\r\n## New Contributors\r\n* @Shmily1368 made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F1\r\n* @tenderzada made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F2\r\n* @Li-Hongcheng made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F7\r\n* @AlvinIsonomia made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F8\r\n* @yh-yao made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F9\r\n* @xbfu made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F11\r\n* @lokinko made their first contribution in https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fpull\u002F12\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fyoungfish42\u002FAwesome-FL\u002Fcommits\u002Fv0.8.0-alpha","2022-09-16T02:57:38"]