Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources. So far FL research has mostly focused on improving the performance, how the algorithmic disparity will be impacted for the model learned from FL and the impact of algorithmic disparity on the utility inconsistency are largely unexplored. In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources). We derive our framework from a constrained multi-objective optimization perspective, in which we learn a model satisfying fairness constraints on all clients with consistent performance. Specifically, we treat the algorithm prediction loss at each local client as an objective and maximize the worst-performing client with fairness constraints through optimizing a surrogate maximum function with all objectives involved. A gradient-based procedure is employed to achieve the Pareto optimality of this optimization problem. Theoretical analysis is provided to prove that our method can converge to a Pareto solution that achieves the min-max performance with fairness constraints on all clients. Comprehensive experiments on synthetic and real-world datasets demonstrate the superiority that our approach over baselines and its effectiveness in achieving both fairness and consistency across all local clients.
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）；Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院）
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
University of Electronic Science and Technology of China（电子科技大学）；Fudan University（复旦大学）；Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；Meta AI；Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University（中山大学计算机科学与技术学院深圳校区）；School of Computer Science Faculty of Engineering The University of Sydney（悉尼大学计算机科学学院）；Generative AI Lab College of Computing and Data Science Nanyang Technological University（南洋理工大学计算与数据科学学院生成式人工智能实验室）
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；JD Explore Academy（京东探索研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
College of Computer Science, Zhejiang University（浙江大学计算机学院）；Tiansuan Lab, Ant Group（蚂蚁集团天算实验室）；College of Computer and Data Science, Fuzhou University（福州大学计算机与大数据学院）
LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction
National Yang Ming Chiao Tung University（国立阳明交通大学）
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
ENSIAS, Mohammed V University in Rabat（拉巴特穆罕默德五世大学ENSIAS学院）；Higher School of Technology, Hassan II University in Casablanca（卡萨布兰卡哈桑二世大学高等技术学院）；School of Science and Engineering, Al Akhawayn University in Ifrane（伊夫兰阿卡韦恩大学科学与工程学院）
RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
College of Intelligence and Computing, Tianjin University（智能与计算学院，天津大学）；State Key Lab. for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Yunnan Key Lab. of Service Computing, Yunnan University of Finance and Economics（云南服务计算重点实验室，云南财经大学）
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization
The Hong Kong Polytechnic University（香港理工大学）；Huazhong University of Science and Technology（华中科技大学）；Michigan State University（密歇根州立大学）；Hohai University（河海大学）
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Center for Applied Statistics, School of Statistics, Renmin University of China（中国人民大学统计学院应用统计中心）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Department of Statistics, Fudan University（复旦大学统计系）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
National University of Defense Technology（国防科技大学）；Hebei University of Technology（河北工业大学）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Argonne National Laboratory（阿贡国家实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；VMWare Research（威睿研究院）；The University of Chicago（芝加哥大学）
Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Taikang Center for Life and Medical Sciences, Wuhan University（武汉大学泰康生命医学中心）
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用重点实验室（东南大学），教育部，中国）；University of the Chinese Academy of Sciences, China（中国科学院大学，中国）；Wuhan University, China（武汉大学，中国）；Academy for Artificial Intelligence, Hong Kong Polytechnic University, Hong Kong, China（人工智能学院，香港理工大学，香港，中国）
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Shandong University, School of Airspace Science（山东大学，空天科学学院）；Beijing Institute of Technology, School of Computer Science（北京理工大学，计算机科学学院）；Sun Yat-sen University, School of Computer Science（中山大学，计算机科学学院）
Balancing Similarity and Complementarity for Federated Learning
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）；Tsinghua University（清华大学）；The University of Auckland（奥克兰大学）；RIKEN（理化学研究所）；Hong Kong Baptist University（香港浸会大学）；The University of Tokyo（东京大学）
College of Computer Science, Zhejiang University（浙江大学计算机学院）；Polytechnic Institute, Zhejiang University（浙江大学理工学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）
