Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients' samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FedFA to tackle federated learning from a distinct perspective of federated feature augmentation. FedFA is based on a major insight that each client's data distribution can be characterized by statistics (i.e., mean and standard deviation) of latent features; and it is likely to manipulate these local statistics globally, i.e., based on information in the entire federation, to let clients have a better sense of the underlying distribution and therefore alleviate local data bias. Based on this insight, we propose to augment each local feature statistic probabilistically based on a normal distribution, whose mean is the original statistic and variance quantifies the augmentation scope. Key to our approach is the determination of a meaningful Gaussian variance, which is accomplished by taking into account not only biased data of each individual client, but also underlying feature statistics characterized by all participating clients. We offer both theoretical and empirical justifications to verify the effectiveness of FedFA. Our code is available at https://github.com/tfzhou/FedFA.
Fair Concurrent Training of Multiple Models in Federated Learning
Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计学院）；Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电子与计算机工程学院）；Electrical and Computer Engineering, Northeastern University（东北大学电子与计算机工程学院）
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）
Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil（精确与技术科学研究所，弗拉维亚联邦大学-UFV，里奥帕拉纳伊巴-MG，巴西）；Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil（计算系，萨o卡洛斯联邦大学，萨o卡洛斯-SP，巴西）
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
Department of Computer Science University of the Western Cape（计算机科学系，西开普敦大学）；Department of Computer Science Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
School of Computer Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Department of New Networks, Peng Cheng Laboratory（鹏城实验室新型网络部）
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
Seoul National University Hospital（首尔国立大学医院）；Chung-Ang University（中央大学）；Korea University（高丽大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）；Yonsei University（延世大学）
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）；School of Mathematical Sciences, Queensland University of Technology（昆士兰科技大学数学科学学院）；Euler Institute, Università della Svizzera italiana (USI)（瑞士意大利语区大学欧拉研究所）；Insubria University（因苏布里亚大学）
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Graduate School of Engineering, Tohoku University（东北大学工学研究生院）；School of Biomedical Engineering, Anhui Medical University（安徽医科大学生物医学工程学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；IHPC, A*STAR（新加坡科技研究局信息通信研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Guangxi Medical University（广西医科大学）；National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国学院）
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）
Synthetic Data Aided Federated Learning Using Foundation Models
The University of Manchester（曼彻斯特大学）；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属机构））；KU Leuven（鲁汶大学）；COSIC, KU Leuven（鲁汶大学计算机与安全工业密码学中心）
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
Institute of Information & Communications Technology Planning & Evaluation (IITP)-ITRC (Information Technology Research Center)（信息与通信技术规划与评估院（IITP）-ITRC（信息技术研究中心））
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
School of Statistics, Renmin University of China（中国人民大学统计学院）；School of Statistics and Data Science, Southeast University（东南大学统计与数据科学学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京高精尖创新中心）
Robust Federated Learning on Edge Devices with Domain Heterogeneity
Department of Computer Science and Engineering, Kyung Hee University, 171-04, Republic of Korea（韩国庆熙大学计算机科学与工程系）；Machine learning department, Mohamed Bin Zayed University of Artificial Intelligence, United Arab Emirates（阿联酋穆罕默德·本·扎耶德人工智能大学机器学习系）
