Heterogeneous Federated Learning

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Overview

Heterogeneous Federated Learning asks how clients can learn together without sharing raw data when their data distributions, tasks, modalities, models, or hardware differ. Common approaches include personalized or clustered models, drift-correcting optimization and adaptive aggregation, and knowledge sharing through prototypes, distillation, or aligned subspaces. Systems methods also adapt client selection, model size, communication, and training schedules to resource limits, with evaluations spanning non-IID image and language benchmarks, multimodal and medical data, and edge or wireless deployments. The central tension is how to gain from shared learning without negative transfer or poor outcomes for individual clients, while keeping communication, computation, privacy, and fairness costs manageable.

Latest papers

CardsList

297 papers35 papers in the last four weeks, up 30% on the four weeks before. 0.2% of all new papers.

  1. DICE: Decentralized Incentive-driven Federated Learning for Efficient & Scalable Customization of Pretrained Models in Web3

    Oct 29, 2026Hong Lin, Zhongle Xie, Lidan Shou +2Blockchain-Based Federated LearningFederated Fine-Tuning

    The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain&Data Security

  2. Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness

    Oct 28, 2026Long Dang, Ali Alfatemi, Kaiqi Xiong +4Privacy-Preserving Federated LearningTrust-Aware Federated Learning

    Cyber Florida, University of South Florida, Tampa, FL, USA · Department of Computer and Information Science, Fordham University, New York, NY, USA · Data Science Department, University of North Texas, Denton, 76207, USA +2

  3. Data Amount Heterogeneity Improves Performance in Federated Learning for Neuroimaging

    Oct 28, 2026Paul Kurian, Tamoghna Chattopadhyay, Abhijith Shaji +3Federated LearningHeterogeneous Federated Learning

    Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA · Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA · University of California, Riverside, CA, USA

  4. SCAFFOLD under Hessian Similarity: Sharp Convergence Rates

    Oct 26, 2026Zhirayr Tovmasyan, Slavomír Hanzely, Egor Shulgin +2Federated LearningHeterogeneous Federated Learning

    King Abdullah University of Science and Technology (KAUST) · CISPA Helmholtz Center for Information Security · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

  5. Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems

    Oct 25, 2026Ting Xu, Henry LeungFederated LearningHeterogeneous Federated Learning

    Department of Biomedical Engineering, University of Calgary, Calgary, Canada · Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada

  6. Federated Nonparametric Regression with Heterogeneous Clients

    Oct 23, 2026Xiaoran Jiang, Anand N. VidyashankarFederated LearningNonparametric Regression

  7. Is High Local Accuracy Enough? Geometric Consensus with Support-Adaptive Statistical Personalization for Federated Learning

    Oct 22, 2026Junyu Lin, Bosong Qi, Pengcheng Shi +1Federated LearningHeterogeneous Federated Learning

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China

  8. After the Federation: Benchmarking Knowledge Portability to Heterogeneous Newcomers

    Oct 19, 2026Xinyuan ZhaoFederated Knowledge DistillationHeterogeneous Federated Learning

    Sun Yat-sen University

  9. CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning

    Oct 19, 2026Junhao Cheng, Yunming Liao, Hongli Xu +2Split Federated LearningFederated Learning

    School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China, 230027 · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China, 215123

  10. DROD: Dual-Ratio Ordered Dropout for Federated Continual Learning on Heterogeneous Devices

    Oct 18, 2026Berk Sezer, Özgür SoysalFederated Continual LearningHeterogeneous Federated Learning

    Middle East Technical University · Stanford University

  11. FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

    Oct 16, 2026Syed Md. Ahnaf Hasan, Md Akil Raihan Iftee, Sajib Mistry +2Prompt Learning for VLMsTest-Time Prompt Tuning

    Center for Computational & Data Sciences (CCDS), Independent University, Bangladesh · Rochester Institute of Technology, USA · Curtin University, Australia

  12. Bridging Compressed Communication and Local Steps via Spectral Method in Federated Learning

    Oct 16, 2026Dmitriy Bystrov, George Chirkov, Daniil Medyakov +2Federated LearningCommunication-Efficient Distributed Training

    Basic Research of Artificial Intelligence Laboratory (BRAIn Lab) · Federated Learning Problems Laboratory · Innopolis University

  13. 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

  14. TALLOP: Joint Optimization of Local Training and Communication Compression for Time-Constrained Federated Learning

    Oct 16, 2026Honoka Anada, Yafei Ou, Shinya Takamaeda-YamazakiFederated LearningCommunication-Efficient Distributed Training

    The University of Tokyo / RIKEN · RIKEN

  15. SPADE-KD: Peer-to-Peer Collaborative Learning under Heterogeneous Settings

    Oct 15, 2026Harikrishna Kuttivelil, Katia ObraczkaFederated Knowledge DistillationAdaptive Knowledge Distillation

    University of California, Santa Cruz

  16. Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets

    Oct 14, 2026Gwen Legate, Rahaf Aljundi, Eugene BelilovskyFederated LearningHeterogeneous Federated Learning

    Concordia University · Mila · Toyota Motor Europe

  17. FedPCD: Priority-Constrained Federated Aggregation

    Oct 9, 2026Yousef Irshaid, Dara Varam, Mohamed I. AlHajriFederated LearningFederated Learning Aggregation

    Department of Computer Science & Engineering, American University of Sharjah · Senseable City Laboratory, Massachusetts Institute of Technology

  18. Best of Both Worlds in Federated LSA: Speedup When Possible, Personalization Always

    Oct 8, 2026Safwan Labbi, Paul Mangold, Eric MoulinesFederated LearningHeterogeneous Federated Learning

    CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France · Mohamed bin Zayed University of Artificial Intelligence, UAE · LRE EPITA, 94270 Le Kremlin-Bicêtre, France

  19. FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning

    Oct 6, 2026Konstantinos Ziliaskopoulos, Alexander Vinel, Jiaqi WangDecision-Focused LearningFederated Learning

    Auburn University

  20. Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices

    Oct 5, 2026Hok Fong Wong, Hoi-To Wai, Chung-Yiu YauLogistic RegressionFederated Learning Aggregation

    Department of CSE, The Chinese University of Hong Kong, Hong Kong SAR · Department of SEEM, The Chinese University of Hong Kong, Hong Kong SAR · Department of ECE, University of Minnesota, Minnesota, USA

  21. Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

    Oct 3, 2026Xingyue Zhao, Wenke Huang, Linghao Zhuang +11Medical Image SegmentationFederated Learning

    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · College of Computing and Data Science, Nanyang Technological University, Singapore +8

  22. Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning

    Oct 3, 2026Ilya Kuruzov, Dmitrii Vishovan, Kirill Novoselov +3Federated LearningFederated Learning Aggregation

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

  23. Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

    Oct 2, 2026Abhijeet Parida, Zhifan Jiang, Pooneh Roshanitabrizi +6Medical Image SegmentationHeterogeneous Federated Learning

    Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, United States · Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain · CIBER-BBN, Instituto de Salud Carlos III, Madrid, Spain +2

  24. Understanding Trajectory Heterogeneity in Federated World Model Learning

    Oct 2, 2026Yipan Wei, Zhaokun Yan, Ziming Hong +2World Model LearningHeterogeneous Federated Learning

    Wuhan University · China Academy of Information and Communications Technology · The University of Sydney +2

  25. Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis

    Oct 2, 2026Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1Selective SSMsFederated Learning

    Purdue University · University of Colorado Colorado Springs · University of Exeter

  26. vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning

    Oct 1, 2026Seok Bin Son, Samuel Yen-Chi Chen, Soohyun Park +1Quantum Machine LearningQuantum Neural Networks

    Korea University Seoul, Korea · Wells Fargo New York, USA · Sookmyung Women’s University Seoul, Korea

  27. 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

  28. FedMIX-P: Mixing Local and Global Preconditioners for Federated Vision and Language Model Training

    Oct 1, 2026Junkang LiuFederated LearningHeterogeneous Federated Learning

    Tianjin University

  29. FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

    Oct 1, 2026Jolle Verhoog, Ali Burak Ünal, Holger CaesarDomain Generalization3D Object Detection

    faculty of Cognitive Robotics, Mechanical Engineering, TU Delft, Delft, Netherlands

  30. Latent Information Sharing for Accelerating Federated Learning

    Oct 1, 2026Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos +2Federated Learning AggregationFederated Learning

    Inha University · University of Toronto · University of Birmingham