Federated Learning (FL) enables decentralized model training across clients without sharing raw data, but its performance degrades under real-world data heterogeneity. Existing methods often fail to address distribution shift across clients and distribution drift over time, or they rely on unrealistic assumptions such as known number of client clusters and data heterogeneity types, which limits their generalizability. We introduce Feroma, a novel FL framework that explicitly handles both distribution shift and drift without relying on client or cluster identity. Feroma builds on client distribution profiles-compact, privacy-preserving representations of local data-that guide model aggregation and test-time model assignment through adaptive similarity-based weighting. This design allows Feroma to dynamically select aggregation strategies during training, ranging from clustered to personalized, and deploy suitable models to unseen, and unlabeled test clients without retraining, online adaptation, or prior knowledge on clients' data. Extensive experiments show that compared to 10 state-of-the-art methods, Feroma improves performance and stability under dynamic data heterogeneity conditions-an average accuracy gain of up to 12 percentage points over the best baselines across 6 benchmarks-while maintaining computational and communication overhead comparable to FedAvg. These results highlight that distribution-profile-based aggregation offers a practical path toward robust FL under both data distribution shifts and drifts.
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）
SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
CISUC/LASI - Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra（科瓦姆布拉大学信息与系统中心/实验室，信息工程系，科瓦姆布拉大学）；INESC TEC and the Faculty of Economy, University of Porto（INESC TEC和波尔图大学经济学院）；Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra（系统与机器人研究所，电子与计算机工程系，科瓦姆布拉大学）
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University, China（浙江大学公共管理学院）
An Information-Theoretic Analysis for Federated Learning under Concept Drift
Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；Zhejiang University–University of Illinois Urbana–Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
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）；Intelligent Transportation Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能交通枢纽）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；Zhongguancun Lab（中关村实验室）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算与通信学院）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智城）
FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability
School of Computing（计算学院）；Mathematical Sciences, University of Leicester, UK（利兹大学数学科学系）；School of Computing, University of Edinburgh, UK（爱丁堡大学计算学院）；Department of Computer Science, University of Exeter, UK（埃克塞特大学计算机科学系）
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Department of Computer & Information Science & Engineering（计算机与信息科学与工程系）；University of Florida（佛罗里达大学）；Frost Institute for Data Science and Computing（数据科学与计算弗罗斯特研究所）；University of Miami（迈阿密大学）
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
Dalhousie University（达尔豪斯大学）；Imagia Cybernetics Inc.（Imagia控制论公司）；Concordia University（康考迪亚大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）；Polish Academy of Sciences（波兰科学院）
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（清华大学深圳国际研究生院，清华大学，深圳，中国）；Shenzhen Research Institute of Big Data, Shenzhen, China（深圳大数据研究院，深圳，中国）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China（香港大学电子与电气工程系，香港，中国）；Yinwang Intelligent Technology Co. Ltd., Shenzhen, China（云网智能科技有限公司，深圳，中国）
Rethinking Client Drift in Federated Learning: A Logit Perspective
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）；Wuhan University（武汉大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
Innopolis University（因诺波利斯大学）；Moscow Independent Research Institute of Artificial Intelligence（莫斯科独立人工智能研究所）；Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Higher School of Economics（高等经济大学）；Trusted AI Research Center, RAS（俄罗斯科学院可信人工智能研究中心）；Steklov Mathematical Institute, RAS（俄罗斯科学院斯捷克洛夫数学研究所）
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
School of Computing and Information Technology, University of Wollongong, Wollongong, Australia（1 计算与信息科技学院，沃林根大学，澳大利亚）；School of Electrical and Computer Engineering, The University of Sydney, Sydney, Australia（2 电气与计算机工程学院，悉尼大学，澳大利亚）；School of Cyber Engineering, Xidian University, Xi’an, China（3 网络工程学院，西安电子科技大学，中国）；QLD Alliance for Agriculture and Food, The University of Queensland, Brisbane, Australia（4 女王艾丽丝州农业与食品联盟，昆士兰大学，澳大利亚）；Principal Research Scientist, CSIRO Data61, Eveleigh, Australia（5 主要研究员，CSIRO Data61，澳大利亚）
Electrical Engineering and Computer Science, University of California at Merced（加州大学梅尔德分校电气工程与计算机科学系）；Department of Applied Mathematics, University of California Merced（加州大学梅尔德分校应用数学系）；Dept. of Innovation and Research, North Carolina State University（北卡罗来纳州立大学创新与研究部门）；Mechatronics, Embedded Systems and Automation (MESA) Lab, Department of Mechanical Engineering, School of Engineering, University of California, Merced, CA（机械工程系工程学院机电一体化、嵌入式系统与自动化（MESA）实验室，加州大学梅尔德分校）
