Non-IID Federated Learning

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Overview

Non-IID federated learning studies how clients can train a shared model when their data differ in labels, features, domains, or changing conditions, while keeping raw data local. Methods include optimization corrections such as FedProx and SCAFFOLD, clustering and personalization, and knowledge distillation or prototypes to share useful information without simply averaging every update. Client selection, compression, privacy mechanisms, and asynchronous or decentralized training address practical limits such as communication, device capacity, and unreliable participation. A central tension remains between a broadly useful global model and accurate, fair models for individual clients; gains depend on the kind of heterogeneity and must be weighed against communication, privacy, and worst-client performance.

Latest papers

CardsList

211 papers17 papers in the last four weeks, down 37% on the four weeks before. 0.1% of all new papers.

  1. FedSEA: Achieving Benefit of Parallelization in Federated Online Learning

    Apr 21, 2026Harekrushna Sahu, Pratik Jawanpuria, Pranay SharmaOnline Convex OptimizationNon-IID Federated Learning

    CMInDS, IIT Bombay, India

  2. Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization

    Apr 14, 2026Li Shen, Yan Sun, Dacheng TaoFederated LearningNon-IID Federated Learning

    School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University · School of Computer Science, Faculty of Engineering, The University of Sydney · Generative AI Lab, College of Computing and Data Science, Nanyang Technological University

  3. The Impact of Federated Learning on Distributed Remote Sensing Archives

    Apr 13, 2026Anand Umashankar, Karam Tomotaki-Dawoud, Nicolai SchneiderFederated LearningNon-IID Federated Learning

    Technische Universität Berlin Berlin, Germany

  4. Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings

    Apr 12, 2026Cristiano Mafuz, Rodrigo SilvaFederated LearningHeterogeneous Federated Learning

  5. Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity

    Apr 10, 2026Yiran Pang, Zhen Ni, Xiangnan ZhongFederated RLHeterogeneous Federated Learning

    Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA

  6. FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

    Apr 8, 2026Huy Q. Le, Loc X. Nguyen, Yu Qiao +3Federated LearningDistribution Shift

    G-LAMP NEXUS Institute, Kyung Hee University · Kyung Hee University

  7. Enhancing Robustness of Federated Learning via Server Learning

    Apr 3, 2026Van Sy Mai, Kushal Chakrabarti, Richard J. La +1Federated LearningByzantine-Robust Federated Learning

    National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA · Tata Consultancy Services Research, Mumbai, Maharashtra 400607, India · the University of Maryland, College Park, MD 20742, USA +1

  8. FecalFed: Privacy-Preserving Poultry Disease Detection via Federated Learning

    Apr 1, 2026Tien-Yu ChiClassificationFederated Learning

    Independent Researcher

  9. Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification

    Mar 31, 2026Mingkun Tan, Xilu Wang, Michael Kloster +1Federated LearningNon-IID Federated Learning

    Biodata Mining Group, Faculty of Technology, University of Bielefeld, Germany · Computer Science Research Centre, University of Surrey, United Kingdom · Phycology Group, Faculty of Biology, University of Duisburg-Essen, Germany

  10. Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs

    Mar 31, 2026Yuxuan Liu, Wenchao Xu, Haozhao Wang +5Federated Graph LearningDynamic Graph Learning

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, China · Division of Integrative Systems and Design, Hong Kong University of Science and Technology, China · School of Computer Science and Technology, Huazhong University of Science and Technology, China

  11. FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation

    Mar 30, 2026Tiantian Wang, Xiang Xiang, Simon S. DuFederated Continual LearningFederated Learning

    Huazhong University of Science and Technology, Wuhan, China · University of Washington, Seattle, USA

  12. Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data

    Mar 30, 2026Yuanqiao Zhang, Tiantian He, Yuan Gao +5Federated LearningSecond-Order Optimization

    Key Laboratory of Collaborative Intelligence Systems of Ministry of Education, Xidian University, Xi’an, Shaanxi, China · Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · State Key Laboratory of ISN, School of Cyber Engineering, Xidian University, Xi’an, Shaanxi, China +2

  13. FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning

    Mar 30, 2026Brianna Mueller, W. Nick StreetFederated LearningNon-IID Federated Learning

    University of Iowa, Iowa City, Iowa

  14. HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfer

    Mar 25, 2026Minjun Kim, Minje KimFederated LearningHeterogeneous Federated Learning

    Promedius Inc.

  15. FedCVU: Federated Learning for Cross-View Video Understanding

    Mar 23, 2026Shenghan Zhang, Run Ling, Ke Cao +2Federated LearningVideo Understanding

    Software College, Northeastern University, Shenyang, China · University of Science and Technology of China, Hefei, China · University of Chinese Academy of Sciences, Beijing, China +1

  16. Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

    Mar 22, 2026Zihan Fang, Qianru Wang, Haonan An +4Mixture-of-Experts Language ModelsFederated Learning Aggregation

    Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China · School of Computer Science and Technology, Xidian University, Xi’an, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong, China +1

  17. FedPDPO: Federated Personalized Direct Preference Optimization for Large Language Model Alignment

    Mar 20, 2026Kewen Zhu, Liping Yi, Zhiming Zhao +3Personalized Language Model AlignmentDirect Preference Optimization

    College of Intelligence and Computing, Tianjin University, Tianjin, China · College of Computing and Data Science, Nanyang Technological University, Singapore

  18. FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy

    Mar 19, 2026Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clément Larose +3Trust-Aware Federated LearningFederated Learning

    Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Departamento de Ciencias Computacionales, Guadalajara, 45017, Jalisco, Mexico · Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey, 64849, N.L., Mexico · CRAN UMR 7039, Université de Lorraine and CNRS, Vandœuvre-lès-Nancy, France +1

  19. Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity

    Mar 19, 2026Jing Liu, Zhengliang Guo, Yan Wang +4Heterogeneous Federated LearningMultimodal Federated Learning

    The University of British Columbia · Fudan University · Duke Kunshan University +5

  20. Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization

    Mar 18, 2026Ratun Rahman, Dinh C. NguyenFederated LearningHeterogeneous Federated Learning

    Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA

  21. FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios

    Mar 17, 2026Andrea Moleri, Christian Internò, Ali Raza +4Federated LearningNon-IID Federated Learning

    Honda Research Institute Europe, Germany · University of Milan-Bicocca, Italy · Bielefeld University, Germany +1

  22. DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning

    Mar 16, 2026Zhiyu Wang, Mohammad Goudarzi, Mingming Gong +1Federated RLNon-IID Federated Learning

    The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia · The Faculty of Information Technology, Monash University, Australia · School of Mathematics and Statistics, The University of Melbourne, Australia

  23. FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data

    Mar 14, 2026Eman M. AbouNassar, Amr Elshall, Sameh AbdulahFederated LearningNon-IID Federated Learning

    Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El Koom, Egypt. · Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin El Koom, Egypt.

  24. Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE

    Mar 13, 2026Holger R. Roth, Sarthak Tickoo, Mayank Kumar +18Financial Fraud DetectionAnomaly Detection

    NVIDIA, Santa Clara, USA · J.P. Morgan, New York, USA · DeepTempo, San Francisco, USA +2

  25. Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation

    Mar 12, 2026Fengyuan Yu, Xiaohua Feng, Yuyuan Li +3Recommender SystemsNon-IID Federated Learning

    Zhejiang University, Hangzhou, China · Hangzhou Dianzi University, Hangzhou, China · OPPO Research Institute, Shenzhen, China

  26. Client-Conditional Federated Learning via Local Training Data Statistics

    Mar 11, 2026Rickard BrännvallFederated LearningNon-IID Federated Learning

    RISE Research Institutes of Sweden

  27. Federated Active Learning Under Extreme Non-IID and Global Class Imbalance

    Mar 11, 2026Chen-Chen Zong, Sheng-Jun HuangActive LearningFederated Learning

    Nanjing University of Aeronautics and Astronautics, China

  28. A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System

    Mar 10, 2026Luyao Zou, Hayoung Oh, Chu Myaet Thwal +3Federated Knowledge DistillationFederated Learning

    College of Computing and Informatics, Sungkyunkwan University, Republic of Korea · Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, Republic of Korea · Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali-3814, Bangladesh +2

  29. FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

    Mar 9, 2026Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh +3Client Selection in Federated LearningNon-IID Federated Learning

    Sapienza University of Rome Via Ariosto 25 Rome, 00185, Italy

  30. FedPrism: Adaptive Personalized Federated Learning under Non-IID Data

    Mar 9, 2026Prakash Kumbhakar, Shrey Srivastava, Haroon R LoneFederated LearningNon-IID Federated Learning

    IISER Bhopal, Bhopal, India