Federated learning is a distributed paradigm that aims at training models using samples distributed across multiple users in a network while keeping the samples on users' devices with the aim of efficiency and protecting users privacy. In such settings, the training data is often statistically heterogeneous and manifests various distribution shifts across users, which degrades the performance of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm that achieves satisfactory performance against distribution shifts in users' samples. To achieve this goal, we first consider a structured affine distribution shift in users' data that captures the device-dependent data heterogeneity in federated settings. This perturbation model is applicable to various federated learning problems such as image classification where the images undergo device-dependent imperfections, e.g. different intensity, contrast, and brightness. To address affine distribution shifts across users, we propose a Federated Learning framework Robust to Affine distribution shifts (FLRA) that is provably robust against affine Wasserstein shifts to the distribution of observed samples. To solve the FLRA's distributed minimax problem, we propose a fast and efficient optimization method and provide convergence guarantees via a gradient Descent Ascent (GDA) method. We further prove generalization error bounds for the learnt classifier to show proper generalization from empirical distribution of samples to the true underlying distribution. We perform several numerical experiments to empirically support FLRA. We show that an affine distribution shift indeed suffices to significantly decrease the performance of the learnt classifier in a new test user, and our proposed algorithm achieves a significant gain in comparison to standard federated learning and adversarial training methods.
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Ecole Polytechnique（巴黎综合理工学院）；Lagrange Mathematics and Computing Research Center（拉格朗日数学与计算研究中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Technology Innovation Institute（技术创新研究院）
Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
Department of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea（电气与计算机工程系，成均馆大学，大韩民国）；Soongsil University, Seoul, Republic of Korea（顺世大学，首尔，大韩民国）
A Federated Many-to-One Hopfield model for associative Neural Networks
Università del Salento（萨莱诺大学）；Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Lecce（意大利国家核物理研究所（INFN）莱切分部）；Istituto Nazionale di Alta Matematica Francesco Severi (INdAM)（弗朗切斯科·塞韦里高等数学国立研究所（INdAM））；Sapienza Università di Roma（罗马智慧大学）
FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
University of Macau（澳门大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University of Advanced Technology（深圳理工大学）；DataStory Information Technology Co., Ltd.（数说信息科技有限公司）
Label-shift robust federated feature screening for high-dimensional classification
Center for Applied Statistics and School of Statistics Renmin University of China（应用统计中心和中国人民大学统计学院）；School of Mathematics and Statistics Xi’an Jiaotong University（西安交通大学数学与统计学院）；Department of Mathematics and Fundamental Research Peng Cheng Laboratory School of Mathematics and Statistics Xi’an Jiaotong University（数学与基础研究部鹏城实验室数学与统计学院）
Principled Federated Random Forests for Heterogeneous Data
Inria（法国国家信息与自动化研究所）；PreMeDICaL（预医学实验室）；Inserm（法国国家医学研究院）；University of Montpellier（蒙彼利埃大学）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；CNRS（国家科学研究中心）；LPSM（巴黎高等师范学院）
Overcoming label shift with target-aware federated learning
RISE Research Institutes of Sweden（瑞典RISE研究机构）；KTH Royal Institute of Technology（皇家理工学院）；Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
Department of Computer Science and Engineering, School of Computing, Kyung Hee University（计算机科学与工程系，计算学院，庆熙大学）；Department of Artificial Intelligence, School of Computing, Kyung Hee University（人工智能系，计算学院，庆熙大学）；Digital Science and Technology Institute, The University of Danang—Vietnam-Korea University of Information and Communication Technology（数字科学与技术研究所，丹那大学—越南-韩国信息与通信技术大学）
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智能网络重点实验室）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
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）
FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
Department of Electrical Engineering and Computer Science, University of Stavanger（电气工程与计算机科学系，斯塔万格大学）；Department of Computer Convergence Software, Korea University（计算机融合软件系，韩国大学）
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）
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智能网络重点实验室）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工学院）
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
AI for Good (AIGO), Italian Institute of Technology（意大利技术研究院AI for Good (AIGO)）；MaLGa-DIBRIS, University of Genoa（热那亚大学MaLGa-DIBRIS）；Department of Computer Science, University of Verona（维罗纳大学计算机科学系）
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Lassonde School of Engineering, York University（约克大学拉萨onde工程学院）
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
SVKM’s Dwarkadas J. Sanghvi College of Engineering（SVKM德瓦尔卡达斯·J·桑格维工程学院）；GSSS Institute of Engineering and Technology for Women（GSSS女子工程技术学院）；Purdue University（普渡大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；NYUAD Research Institute（纽约大学阿布扎比研究所）；University of Southern Denmark（南丹麦大学）；Tallinn University of Technology（塔林理工大学）；NASA Ames Research Center（NASA艾姆斯研究中心）；Universities Space Research Association（大学空间研究协会）
