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

299 papers22 papers in the last four weeks, down 42% on the four weeks before. 0.1% of all new papers.

  1. 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

  2. Federated Learning for Privacy-Preserving Medical AI

    Mar 16, 2026Tin HoangDifferentially Private Federated LearningDifferential Privacy

    School of Computer Science and Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK

  3. SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning

    Mar 16, 2026Ran Tao, Qiugang Zhan, Shantian Yang +3Federated LearningHeterogeneous Federated Learning

    Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, China · Engineering Research Center of Intelligent Finance, Ministry of Education, China · Kash Institute of Electronics and Information Industry, China +2

  4. Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation

    Mar 16, 2026Meilu Zhu, Yuxing Li, Zhiwei Wang +1Federated LearningHeterogeneous Federated Learning

    Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

  5. Domain-Skewed Federated Learning with Feature Decoupling and Calibration

    Mar 15, 2026Huan Wang, Jun Shen, Jun Yan +1Federated LearningHeterogeneous Federated Learning

    School of Computing and Information Technology, University of Wollongong, Australia · School of Computing and Information Systems, Singapore Management University, Singapore

  6. 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.

  7. CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction

    Mar 13, 2026Gang Hu, Yinglei Teng, Pengfei Wu +1Federated LearningHeterogeneous Federated Learning

  8. Few-for-Many Personalized Federated Learning

    Mar 12, 2026Ping Guo, Tiantian Zhang, Xi Lin +3Federated LearningHeterogeneous Federated Learning

    City University of Hong Kong · CityU Shenzhen Research Institute · Hong Kong Metropolitan University +3

  9. Personalized Federated Learning via Gaussian Generative Modeling

    Mar 12, 2026Peng Hu, Jianwei MaFederated LearningHeterogeneous Federated Learning

    P. Hu is with the School of Mathematics, Harbin Institute of Technology, Harbin, China. · J. Ma is with the Institute for Artificial Intelligence and the School of Earth and Space Sciences, Peking University, Beijing, China, and also with the Institute for Artificial Intelligence and the School of Mathematics, Harbin Institute of Technology, Harbin, China.

  10. 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

  11. FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

    Mar 11, 2026Md Sirajul Islam, Isabelle G Chapman, N I Md Ashafuddula +4Online Federated LearningFederated Learning

    School of Computing and Informatics, University of Louisiana at Lafayette, USA · Department of Computer and Information Sciences, University of Delaware, USA · Department of Computer Science, University of Illinois at Urbana-Champaign, USA

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

    Mar 11, 2026Rickard BrännvallFederated LearningNon-IID Federated Learning

    RISE Research Institutes of Sweden

  13. 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

  14. LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction

    Mar 7, 2026Chia-Fu Lin, Yi-Ju TsengFederated LearningHeterogeneous Federated Learning

    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

  15. Resource-Adaptive Federated Text Generation with Differential Privacy

    Mar 7, 2026Jiayi Wang, John Gounley, Heidi HansonDifferentially Private Federated LearningSynthetic Data Generation for Language Models

    Oak Ridge National Laboratory Oak Ridge, TN, USA

  16. A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines

    Mar 5, 2026Bowen Li, Xiufeng Liu, Maria Sinziiana AstefanoaeiWind Power ForecastingClustered Federated Learning

    IT University of Copenhagen, Denmark. · Technical University of Denmark (DTU), Denmark

  17. Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding

    Mar 5, 2026Maximilian Hahn, Alina Zajak, Dominik Heider +1Causal DiscoveryCausal Discovery with Latent Confounding

    University of Münster, Institute of Medical Informatics, Münster, Germany

  18. Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition

    Mar 5, 2026Mengze Hong, Yi Gu, Di Jiang +4Language Model MergingAutomatic Speech Recognition

    Hong Kong Polytechnic University · Ant Group · Shanghai Jiao Tong University +2

  19. FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation

    Mar 5, 2026Min Tan, Junchao Ma, Yinfu Feng +6Multimodal Federated LearningFederated Learning

    Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University · Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University · Alibaba International Digital Commerce Group +1

  20. Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation

    Mar 5, 2026Hong Liu, Dong Wei, Qian Dai +3Federated LearningMultimodal Federated Learning

    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, Fujian, China · Jarvis Research Center, Tencent YouTu Lab, Shenzhen, 518075, Guangdong, China · Department of Computer Science at School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China +1

  21. Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients

    Mar 3, 2026Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen +4Federated LearningPrototype Learning

    Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China +1

  22. Decentralized Federated Learning by Partial Message Exchange

    Mar 2, 2026Shan Sha, Shenglong Zhou, Xin Wang +2Communication-Efficient Distributed TrainingFederated Learning

    School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, China. · Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, U.K.

  23. DeepAFL: Deep Analytic Federated Learning

    Feb 28, 2026Jianheng Tang, Yajiang Huang, Kejia Fan +8Gradient-Free OptimizationFederated Learning

    Peking University, PRC · Key Lab of High Confidence Software Technologies (PKU), Ministry of Education, PRC · Central South University, PRC +4

  24. An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks

    Feb 27, 2026Mohsen Tajgardan, Atena Shiranzaei, Mahdi Rabbani +2Unsupervised Anomaly DetectionAnomaly Detection

    Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran · Department of Computer Engineering, Faculty of Industry and Mining (Khash), Sistan and Baluchestan University, Zahedan, Iran · Canadian Institute for Cybersecurity, University of New Brunswick, NB, Canada +1

  25. FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning

    Feb 27, 2026Jieming Bian, Lei Wang, Letian Zhang +1Federated Fine-TuningFederated Learning

    University of Florida, Gainesville, FL 32611 · Middle Tennessee State University Murfreesboro, TN 37132

  26. FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments

    Feb 26, 2026Anik Pramanik, Murat Kantarcioglu, Vincent Oria +1Clustered Federated LearningFederated Learning

    New Jersey Institute of Technology, USA · Virginia Tech, USA

  27. Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity

    Feb 26, 2026Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn KuFederated Conformal PredictionUncertainty Quantification

    Department of Computer Science and Software Engineering, Auburn University

  28. Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems

    Feb 25, 2026Ayush Mohanty, Paritosh Ramanan, Nagi GebraeelRoot Cause AnalysisHeterogeneous Federated Learning

    School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 · School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74708

  29. FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning

    Feb 24, 2026Alina Devkota, Jacob Thrasher, Donald Adjeroh +2Federated LearningFederated Learning Aggregation

    West Virginia University · University of Aberdeen · Fogsphere (Redev.AI)

  30. ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learning

    Feb 24, 2026Duowen Chen, Yan WangFederated LearningHeterogeneous Federated Learning

    Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University