Novel clustered federated learning based on local loss
LMIB, School of Mathematical Sciences, Beihang University（力学系，数学科学学院，北航）；College of Science, China Agricultural University（中国农业大学，理学院）；School of Mathematical Sciences, Nanjing Normal University（南京师范大学，数学科学学院）；School of Mathematical Sciences, Beihang University（力学系，数学科学学院，北航）
Endong Gu, Yongxin Chen, Hao Wen, Xingju Cai, Deren Han
This paper proposes LCFL, a novel clustering metric for evaluating clients' data distributions in federated learning. LCFL aligns with federated learning requirements, accurately assessing client-to-client variations in data distribution. It offers advantages over existing clustered federated learning methods, addressing privacy concerns, improving applicability to non-convex models, and providing more accurate classification results. LCFL does not require prior knowledge of clients' data distributions. We provide a rigorous mathematical analysis, demonstrating the correctness and feasibility of our framework. Numerical experiments with neural network instances highlight the superior performance of LCFL over baselines on several clustered federated learning benchmarks.
On the Convergence of Clustered Federated Learning
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；University of Washington, Seattle（西雅图华盛顿大学）；University of Maryland, College Park（马里兰大学帕克分校）
Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models
1 School of Artificial Intelligence；Computer Science, Jiangnan University 2 Antai College of Economics \& Management, Shanghai Jiao Tong University 3 School of Computer Science, Shanghai Jiao Tong University 4 Cloud Computing；Distributed Systems Laboratory, University of Melbourne
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
Graduate School of Science and Technology, University of Tsukuba（茨川大学理学技术研究生院）；Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba（茨川大学先进研究所人工智能研究中心）；Institute of Systems and Information Engineering, University of Tsukuba（茨川大学系统与信息工程研究所）
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Korea University（高丽大学）
FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments
Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan（计算机科学系，拉合尔管理科学大学，拉合尔，巴基斯坦）
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）；School of Engineering, Westlake University（西湖大学工学院）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
Texas A&M University（德克萨斯农工大学）；Texas A&M University at Qatar（德克萨斯农工大学卡塔尔分校）；Qatar Foundation Research, Development and Innovation RDI（卡塔尔基金会研究、开发与创新（RDI））；Tennessee Tech University（田纳西理工大学）
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT
Information Countermeasure Technique Institute（信息对抗技术研究所）；School of Cyberspace Science（网络空间科学学院）；Faculty of Computing（计算学部）；Harbin Institute of Technology（哈尔滨工业大学）
Distilling A Universal Expert from Clustered Federated Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼大学技术学院澳大利亚人工智能研究所）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机科学学院）
Auction Based Clustered Federated Learning in Mobile Edge Computing System
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Cyberspace Security Research Center, Peng Cheng Laboratory（鹏城实验室网络空间安全研究中心）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电气与数据工程学院）；Department of Computer Science and Technology, Fuzhou University（福州大学计算机科学与技术学院）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology（吕勒奥理工大学计算机、电气与空间工程系）
A Joint Gradient and Loss Based Clustered Federated Learning Design
University of Miami（迈阿密大学）；Frost Institute for Data Science and Computing, University of Miami（迈阿密大学弗罗斯特数据科学与计算研究所）；Zhejiang University（浙江大学）；North Carolina State University（北卡罗来纳州立大学）
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Chongqing University（重庆大学）；College of Computer Science, Chongqing University（重庆大学计算机学院）；School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；Baidu Research, Baidu Inc.（百度研究院，百度公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；BOSS Zhipin（BOSS直聘）
