Non-IID Federated Learning

Ask about this topic

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

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

  1. FedBST: Bilateral Self-Training for Unsupervised Federated Classification

    Oct 22, 2026Aijing Yu, Kuangpu Guo, Jian Liang +1Federated LearningSelf-Training

    School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China +2

  2. From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering

    Oct 21, 2026Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari +2Federated LearningNon-IID Federated Learning

    Sharif University of Technology · Tehran Institute for Advanced Studies (TeIAS)

  3. Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulation

    Oct 19, 2026Yuankun Xia, Hui Wang, Ling Yuan +3Federated LearningFederated Domain Generalization

    School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China

  4. Peer-Relative Trust-Guided Update Screening for Robust Semi-Decentralized Federated Learning under Label-Flipping Attacks

    Oct 19, 2026Ajay Kumar Shrestha, Sandhya JoshiTrust-Aware Federated LearningFederated Learning

    Vancouver Island University, Nanaimo BC V9R 5S5, Canada

  5. AdaFedXplain: Adaptive Federated Learning with Explainable AI for Ransomware Detection

    Oct 18, 2026Md. Emran Biswas, Md. Delowar Hossain, Mst. Tanbin Yasmin Tanny +3Malware DetectionFederated Learning

    Department of Electronics and Communication Engineering · Department of Computer Science and Engineering, Hajee Mohammad Danesh Science & Technology University, Bangladesh · Department of Data Science and Engineering, Bangladesh University of Business and Technology, Bangladesh +1

  6. Federated Learning for Malware Image Classification under Data Heterogeneity

    Oct 18, 2026Victor Taiwo, Cemal Nisan, Muhammad Athallah +2Non-IID Federated LearningFederated Learning

    Department of Computer Engineering, Koç University, Istanbul, Türkiye · Informatics Institute, Istanbul Technical University, Istanbul, Türkiye

  7. Client-Aware Security Adaptive Early Exit Vision Transformers for Heterogeneous Federated Learning

    Oct 16, 2026Sahar Refaei Ghalibafan, Samira Nazari, Christian Herglotz +2Early-Exit Neural NetworksHeterogeneous Federated Learning

    Brandenburg University of Technology, Cottbus, Germany · University of Zanjan, Zanjan, Iran · Tallinn University of Technology, Tallinn, Estonia +1

  8. A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization

    Oct 14, 2026Ekkehard Schnoor, Malik Tiomoko, Alexander JungFederated LearningNon-IID Federated Learning

    Aalto University, Espoo, Finland · Huawei Noah’s Ark Lab, Paris, France

  9. FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences

    Oct 13, 2026Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1Federated LearningNon-IID Federated Learning

    Graduate School of Engineering, The University of Osaka, Osaka, Japan · Faculty of Engineering Science, Kansai University, Osaka, Japan

  10. Federated Coreset Subsampling for Distributed Memory Banks

    Oct 12, 2026Jason Witzemann, Daniel Pröll, Sebastian HegenbartCoreset SelectionAnomaly Detection

    Digital Factory Vorarlberg GmbH, Dornbirn, Austria · University of Salzburg, Salzburg, Austria · Vorarlberg University of Applied Sciences, Dornbirn, Austria

  11. Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning

    Oct 11, 2026Yuriy Dorn, Dmitrii Vishovan, Kirill Novoselov +3Federated LearningOnline Federated Learning

    Lomonosov Moscow State University · Innopolis University · Moscow Independent Research Institute of Artificial Intelligence +3

  12. Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering

    Oct 5, 2026Haemin Park, Diego Klabjan, Martin W. Braun +2ClusteringDeep Clustering

    Northwestern University · Intel Corporation

  13. RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

    Oct 2, 2026Alessandro LicciardiWavelet Scattering TransformsClustered Federated Learning

    Department of Mathematical Sciences Politecnico di Torino, Turin, Italy

  14. FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

    Oct 1, 2026Wentao Yue, Tianyou Lai, Hongji Li +2Non-IID Federated LearningFederated Domain Generalization

  15. FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification

    Sep 30, 2026Barış Büyüktaş, Begüm DemirRemote Sensing Image UnderstandingFederated Learning Aggregation

    Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany · BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany

  16. Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

    Sep 30, 2026Muzaffer Citir, Hiroki Nishikawa, Sangyoung ParkClient Selection in Federated LearningNon-IID Federated Learning

    Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan

  17. Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training

    Sep 28, 2026Deepthy K. Bhaskar, VP Binu, B MinimolFederated Learning AggregationClient Selection in Federated Learning

    Department of Computer Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India. · Department of Biomedical Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India.

  18. Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulation

    Sep 26, 2026Biprodip Pal, Kaushik Roy, Yanming Zhu +3Federated Knowledge DistillationNon-IID Federated Learning

    School of ICT, Griffith University, Australia · CSIRO Robotics, CSIRO, Australia

  19. Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellations

    Sep 25, 2026Mohamed Shaaban, Mohamed Elmahallawy, Marius Bernahrndt +1Federated LearningMixture of Experts

    School of Engineering and Applied Science, Washington State University, Richland, WA 99354, USA · Institute for Software Technology, German Aerospace Center (DLR), Porz, 51147 Cologne, Germany

  20. FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification

    Sep 25, 2026Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi +1Histopathology Image ClassificationFederated Learning

    Department of Computer Science, International Islamic University Chittagong (IIUC), Chittagong, Bangladesh · Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh · Department of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh, 11437, Saudi Arabia +2

  21. On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

    Sep 22, 2026Leonardo F. Toso, James Anderson, Nirupam Gupta +1Byzantine-Robust Federated LearningNon-IID Federated Learning

    Columbia University, USA · University of Copenhagen, Denmark · Sorbonne Université and Université Paris Cité, CNRS, LPSM, France

  22. Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data

    Sep 18, 2026Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik +1Deep ClusteringFederated Learning

    Department of Advanced Computing Sciences (DACS) Maastricht University Maastricht, Netherlands

  23. FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

    Sep 17, 2026Herlock Rahimi, Dionysis KalogeriasFederated LearningFederated Learning Aggregation

  24. Distributionally Robust Federated Learning with Multi-Source Data

    Sep 17, 2026Yingzhu Liu, Zhongkui Li, Pengcheng You +1Distributionally Robust OptimizationNon-IID Federated Learning

    Department of Control Science and Systems Engineering, Peking University, Beijing, China. · Engineering and Technology Institute Groningen and the Jan C. Willems Center for Systems and Control, University of Groningen, The Netherlands.

  25. FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning

    Sep 17, 2026Yasmeen Afzal, Jeremiah D. Deng, Haibo ZhangNon-IID Federated LearningFederated Learning

    School of Computing, University of Otago, New Zealand · School of Computer Science and Engineering, University of New South Wales, Australia

  26. Federated Soft Clustering via Generalized Total Variation Minimization

    Sep 16, 2026Shamsiiat Abdurakhmanova, Alexander JungGaussian Mixture ModelsNon-IID Federated Learning

    Department of Computer Science, Aalto University, Espoo, Finland

  27. Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation

    Sep 15, 2026Polycarpo Souza Neto, José Mairton Barros da Silva Júnior, Charles Casimiro CavalcanteFederated Knowledge DistillationNon-IID Federated Learning

    Universidade Federal do Cear´a, Fortaleza, CE, Brazil · Uppsala University, Uppsala, Sweden

  28. Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification

    Sep 15, 2026Yusuf Ozturk, Bengisu Atli, Enes Goktekin +2Federated Learning AggregationNon-IID Federated Learning

    Antalya Bilim University, Electrical and Electronics Engineering Dep. Antalya, Türkiye · Antalya Bilim University, Computer Engineering Dep. Antalya, Türkiye · Ankara University, Graduate School of Natural and Applied Sciences, Ankara, Türkiye +1

  29. Adaptive Bayesian Partner Selection for Federated Clinical Centers

    Sep 15, 2026Navid Seidi, Satyaki Roy, Sajal K. DasHealthcareCommunication-Efficient Distributed Training

    Department of Computer Science Missouri University of Science and Technology Rolla, MO, USA · Department of Mathematical Sciences University of Alabama in Huntsville Huntsville, AL, USA

  30. FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning

    Sep 14, 2026Changkun Lin, Junxiao WangClass-Imbalanced LearningNon-IID Federated Learning

    Guangzhou University, China