We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models. These threats along with the non-IID nature of data sets can considerably diminish the accuracy of the FL model. Our proposed MoE-FL setup relies on the trust between users and the server where the users share a portion of their public data sets with the server. The server applies a robust aggregation method by solving the optimization problem or the Softmax method to highlight the outlier cases and to reduce their adverse effect on the FL process. Our experiments illustrate that MoE-FL outperforms the performance of the traditional aggregation approach for high rate of poisoned data from attackers.
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
School of Computer Science and Statistics, Trinity College Dublin, Ireland（特里尼蒂学院都柏林分校计算机科学与统计学学院）；School of Computer Science, University College Dublin, Ireland（都柏林大学学院计算机科学学院）
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
School of Automation, Central South University（中南大学自动化学院）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电子工程系）
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
Division of Electrical and Computer Engineering, Louisiana State University（电气与计算机工程系，路易斯安那州立大学）；Division of Computer Science and Engineering, Louisiana State University（计算机科学与工程系，路易斯安那州立大学）；Department of Data Science, New Jersey Institute of Technology（数据科学系，新泽西理工学院）；Department of Electrical and Computer Engineering, University of Louisiana at Lafayette（电气与计算机工程系，路易斯安那大学拉斐特分校）；Department of Electrical and Electronic Engineering, Imperial College London（电气与电子工程系，伦敦帝国理工学院）
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
Hong Kong JC STEM Lab of Smart City（香港JC STEM实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电子与计算机工程系）
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
Beijing Univ. of Posts and Telecomm.（北京邮电大学）；Beijing Jiaotong University（北京交通大学）；Beijing Univ. of Aeronautics and Astronautics（北京航空航天大学）；Tsinghua University（清华大学）
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Sun Yat-Sen University, China（中山大学）；The University of Hong Kong, Hong Kong（香港大学）；Nanyang Technological University, Singapore（南洋理工大学）；National University of Defense Technology, China（国防科技大学）
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电子与计算机工程系）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（罗伯特·博世网络物理系统中心，印度科学学院）；School of Computing, Newcastle University（纽卡斯尔大学计算学院）
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；PKU-OCTA Laboratory for Blockchain and Privacy Computing（区块链与隐私计算实验室）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Booth School of Business University of Chicago（芝加哥大学商学院）；Volgenau School of Engineering, George Mason University（乔治·马歇尔大学工程学院）；Department of Systems Engineering and Operations Research George Mason University（乔治·马歇尔大学系统工程与运营管理系）
Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning
Department of Computer Science and Engineering, Korea University, Republic of Korea（韩国大学计算机科学与工程系）；KT, Republic of Korea（韩国KT）；School of Software, Soongsil University, Republic of Korea（顺天大学软件学院）
