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. Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

    Feb 24, 2026Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1Cross-Modality Medical Image SegmentationData Augmentation

    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway. · Central Staff, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.

  2. Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning

    Feb 24, 2026Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt +1Client Selection in Federated LearningHeterogeneous Federated Learning

    University of Oregon, US · Boston College, US · Microsoft Research, India

  3. Communication-Efficient Personalized Adaptation via Federated-Local Model Merging

    Feb 20, 2026Yinan Zou, Md Kamran Chowdhury Shisher, Christopher G. Brinton +1Federated LearningFederated Fine-Tuning

    Elmore Family School of Electrical and Computer Engineering, Purdue University.

  4. ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation

    Feb 19, 2026Chuiyang Meng, Ming Tang, Vincent W. S. WongFederated Fine-TuningMemory-Efficient Fine-Tuning

    Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada · Department of Computer Science and Engineering, Southern University of Science and Technology, China

  5. Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

    Feb 18, 2026Srikumar Nayak, James WalmesleyCyber Threat IntelligenceFederated Graph Learning

    Incedo Inc., New Jersey, USA · Indian Institute of Technology, Chennai, Tamil Nadu, India · University of Kent, Kent, UK

  6. On the Geometric Coherence of Global Aggregation in Federated Graph Neural Networks

    Feb 17, 2026Chethana Prasad Kabgere, Shylaja SSFederated Graph LearningFederated Learning Aggregation

    Department of Computer Science and Engineering, PES University, Bengaluru, Karnataka, 560085, India

  7. Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data

    Feb 17, 2026Sharmad Kalpande, Saurabh Shirke, Haroon R. LoneFederated LearningEmotion Recognition

    Indian Institute of Science Education and Research Bhopal, India

  8. pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI

    Feb 16, 2026Qingqian Yang, Hao Wang, Sai Qian Zhang +6Federated Learning AggregationNon-IID Federated Learning

    Stevens Institute of Technology · Hoboken, NJ, USA · New York University +9

  9. Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability

    Feb 13, 2026Ayse Tursucular, Ayush Mohanty, Nazal Mohamed +1Federated LearningNonlinear System Identification

    Georgia Institute of Technology Atlanta, GA, USA

  10. An Adaptive Differentially Private Federated Learning Framework

    Feb 6, 2026Jin Wang, Hui Ma, Yajun Zhang +4Non-IID Federated LearningDifferentially Private Federated Learning

    Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi 830017, China · College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China · Jilin University, Changchun 130012, China

  11. ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge

    Jan 20, 2026Xiaohong Yang, Tong Xie, Minghui Liwang +6Federated Fine-TuningFederated Learning

    the School of Informatics, Xiamen University, Fujian, China · the National University of Singapore · the Department of Control Science and Engineering, the National Key Laboratory of Autonomous Intelligent Unmanned Systems, and also with Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Tongji University, Shanghai, China +2

  12. A Privacy-Preserving Federated Learning Method for Heterogeneous Clients

    Nov 8, 2025Yusaku Negoya, Feifei Cui, Zilong Zhang +3Privacy-Preserving Federated LearningDifferentially Private Federated Learning

    Department of Computer and Network Engineering, The University of Electro-Communications, Tokyo, Japan · School of Computer Science and Technology, Hainan University, Haikou 570228, China. · Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA +1

  13. FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements

    Oct 14, 2025Xiao Yang, Dengbo He, Duoxun Tang +2Federated Domain GeneralizationFederated Learning

    Systems Hub, the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

  14. An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data

    Sep 5, 2025Jiaojiao Zhang, Yizhao Fan, Yuqi Xu +1Communication-Efficient Distributed TrainingNon-IID Federated Learning

    Intelligent Computing Research Center, Great Bay University, Dongguan, China · Center for Machine Learning Research, Peking University, Beijing, China

  15. FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints

    Sep 1, 2025Lishan Yang, Wei Emma Zhang, Nam Kha Nguygen +4Federated Fine-TuningFederated Learning Aggregation

    Adelaide University · Central China Normal University · Harbin Institute of Technology

  16. FAIRVAR: Fair Federated Learning via Variance Regularization

    Aug 16, 2025Zahra Kharaghani, Ali Dadras, Tommy LöfstedtAlgorithmic FairnessFederated Learning

    Department of Computing Science, Umeå University, Umeå, Sweden · Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden

  17. FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

    Aug 16, 2025You Hak Lee, Keming Fan, Xiaofan Yu +4Compute-in-MemoryFederated Learning Aggregation

    University of California San Diego, USA

  18. Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion

    Jun 27, 2025Ying Zhang, Yu Zhao, Xuhui Sui +5Multimodal Federated LearningMissing-Modality Learning

    College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1

  19. Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI

    May 28, 2025Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7Federated Learning AggregationDifferentially Private Stochastic Gradient Descent

    German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye · Medical Research Group, MLCommons, San Francisco, CA, USA +2

  20. Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation

    May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning AggregationIndependent Component Analysis

    University of Toronto · National University of Singapore · Rutgers University

  21. FedHQ: Hybrid Runtime Quantization for Federated Learning

    May 17, 2025Zihao Zheng, Ziyao Wang, Jiawei Yang +7Federated LearningAdaptive Quantization

    Peking University · University of Maryland

  22. Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning

    May 16, 2025Shudi Weng, Chao Ren, Ming Xiao +1Federated LearningHeterogeneous Federated Learning

    Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 11428 Stockholm, Sweden

  23. Unified Robust and Convergent Over-The-Air Federated Learning under Channel Noise and Heterogeneity

    Mar 25, 2025Zubair Shaban, Nazreen Shah, Ranjitha PrasadFederated LearningOver-the-Air Federated Learning

    ECE dept., IIIT Delhi, New Delhi.

  24. Correcting Before Adapting: Gradient Correction for Federated Adaptive Optimization

    Feb 4, 2025Evan Chen, Shiqiang Wang, Dong-Jun Han +2Federated LearningHeterogeneous Federated Learning

    Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN · IBM T. J. Watson Research Center, Yorktown Heights, NY, USA · Department of Computer Science and Engineering, Yonsei University, Seoul, Korea

  25. MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

    Sep 9, 2024Jianyi Zhang, Hao Frank Yang, Ang Li +5Multimodal Federated LearningNon-IID Federated Learning

    Duke University · Johns Hopkins University · University of Maryland College Park +1

  26. Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

    Jul 23, 2024Xinghao Wu, Xuefeng Liu, Jianwei Niu +4Prompt LearningFederated Learning

    School of Computer Science and Engineering, Beihang University, Beijing, China · Zhongguancun Laboratory, Beijing, China · Sichuan University, Sichuan, China +1

  27. Personalized Additive Modeling for Multi-level Federated Learning

    May 26, 2024Shutong Chen, Guodong Long, Tianyi Zhou +3Non-IID Federated LearningFederated Learning

    University of Technology Sydney, Australia · Mohamed bin Zayed University of Artificial Intelligence, UAE · The Hong Kong Polytechnic University, China

  28. The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication

    May 19, 2024Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5Communication-Efficient Distributed TrainingDistributed Optimization

    Toyota Technological Institute, Chicago · Stanford University · CISPA Helmholtz Center +1

  29. Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

    Nov 6, 2023Yipeng Li, Xinchen LyuNon-IID Federated LearningStochastic Optimization Convergence

    National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing, 100876, China