Federated learning is a distributed machine learning paradigm that trains a global model for prediction based on a number of local models at clients while local data privacy is preserved. Class imbalance is believed to be one of the factors that degrades the global model performance. However, there has been very little research on if and how class imbalance can affect the global performance. class imbalance in federated learning is much more complex than that in traditional non-distributed machine learning, due to different class imbalance situations at local clients. Class imbalance needs to be re-defined in distributed learning environments. In this paper, first, we propose two new metrics to define class imbalance -- the global class imbalance degree (MID) and the local difference of class imbalance among clients (WCS). Then, we conduct extensive experiments to analyze the impact of class imbalance on the global performance in various scenarios based on our definition. Our results show that a higher MID and a larger WCS degrade more the performance of the global model. Besides, WCS is shown to slow down the convergence of the global model by misdirecting the optimization.
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Sardar Patel Institute of Technology（萨达尔·帕特尔理工学院）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi（印度理工学院德里分校 Amar Nath and Shashi Khosla 信息技术学院）；Department of Electrical Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校电气工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Department of Radiology, University of Cambridge（放射学系，剑桥大学）
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Duke University（杜克大学）；University of Maryland, College Park（马里兰大学帕克分校）；George Mason University（乔治梅森大学）；Yale University（耶鲁大学）；The State University of New York at Buffalo（纽约州立大学布法罗分校）
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Huazhong University of Science and Technology（华中科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Hong Kong University of Science and Technology（香港科技大学）
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Washington State University（华盛顿州立大学）；Princeton University（普林斯顿大学）；IBM Research（IBM研究院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣迭戈分校）
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Peking University（北京大学）；Key Laboratory of Machine Perception (Ministry of Education), Peking University（北京大学机器感知重点实验室（教育部））；National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
FedReLa: Imbalanced Federated Learning via Re-Labeling
School of Computing and Information Systems, University of Melbourne, Victoria, Australia（墨尔本大学计算机与信息系统学院）；School of Statistics and Data Science, LPMC, KLMDASR, and LEBPS, Nankai University（南开大学统计与数据科学学院）；School of Mathematics and Statistics, University of Melbourne, Victoria, Australia（墨尔本大学数学与统计学学院）
Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
University of California, Riverside（加州大学河滨分校）；Indian Institute of Science（印度科学研究院）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；DEVCOM Army Research Laboratory（陆军研究实验室）
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea（人工智能跨学科项目，首尔国立大学，韩国）；Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea（核医学系，首尔国立大学医学院，韩国）；Brightonix Imaging Inc. Seoul, Republic of Korea（Brightonix影像公司，首尔，韩国）
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
The Pennsylvania State University（宾夕法尼亚州立大学）；State University of New York at Albany（纽约州立大学阿尔巴尼分校）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Dalian University of Technology（大连理工大学）
FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios
Shenzhen University（深圳大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
The Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）；Sony AI（索尼人工智能）；The Hong Kong Polytechnic University（香港理工大学）；Northwestern Polytechnical University（西北工业大学）
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Northwestern University（西北大学）；University of Technology Sydney（悉尼科技大学）
FedSampling: A Better Sampling Strategy for Federated Learning
Tsinghua University（清华大学）；Microsoft Research Asia（微软亚洲研究院）；Sony AI（索尼人工智能）；Zhongguancun Laboratory（中关村实验室）；Institute for Precision Medicine of Tsinghua University（清华大学精准医学研究院）
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
National Engineering Laboratory for Big Data Analytics (NEL-BDA)（大数据分析国家工程实验室）；School of Mathematics and Statistics（数学与统计学学院）；Ministry of Education Key Lab for Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）
