Federated Learning

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Overview

Federated learning studies how multiple devices or institutions can train models collaboratively while keeping data local, for example across hospitals, phones, or companies. A common approach, FedAvg, aggregates local model updates; related methods use personalization or client clustering, split learning, model compression, and lightweight adapters such as LoRA. Differential privacy and secure aggregation can limit exposure of updates, while robust aggregation and client-selection methods address malicious participants and uneven data or resources. The central challenge is balancing accuracy and fairness across heterogeneous clients with privacy, security, communication cost, and reliable evaluation; keeping data local alone does not guarantee privacy or strong performance.

Latest papers

CardsList

961 papers112 papers in the last four weeks, up 1% on the four weeks before. 0.6% of all new papers.

  1. SecureAFL: Secure Asynchronous Federated Learning

    Apr 4, 2026Anjun Gao, Feng Wang, Zhenglin Wan +3Federated LearningAsynchronous Federated Learning

    University of Louisville, Louisville, USA · Northeastern University, Shenyang, China · National University of Singapore, Singapore, Singapore +1

  2. BlazeFL: Fast and Deterministic Federated Learning Simulation

    Apr 4, 2026Kitsuya Azuma, Takayuki NishioFederated LearningML Reproducibility

    Institute of Science Tokyo, Tokyo, Japan

  3. 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

  4. Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis

    Apr 3, 2026Guangyu Sun, Wenhan Wu, Zhishuai Guo +3Autism Spectrum DisorderPrivacy-Preserving ML

    Institute of Artificial Intelligence, University of Central Florida · Department of Computer Science, University of North Carolina at Charlotte · Department of Computer Science, Northern Illinois University +2

  5. BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy

    Apr 2, 2026Abhilash Kar, Basisth Saha, Tanmay Sen +1Multimodal Survival AnalysisFederated Learning

    SQC and OR Unit, Indian Statistical Institute, Kolkata, India · Department of Statistics, Presidency University, Kolkata, India

  6. F3DGS: Federated 3D Gaussian Splatting for Decentralized Multi-Agent World Modeling

    Apr 2, 2026Morui Zhu, Mohammad Dehghani Tezerjani, Mátyás Szántó +33D Reconstruction3D Gaussian Splatting

    University of North Texas · Budapest University of Technology and Economics

  7. Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization

    Apr 1, 2026Hao Fang, Wenbo Yu, Bin Chen +4Gradient Inversion AttacksFederated Learning

    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China · Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

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

    Apr 1, 2026Tien-Yu ChiClassificationFederated Learning

    Independent Researcher

  9. Loss Gap Parity for Fairness in Heterogeneous Federated Learning

    Mar 31, 2026Brahim Erraji, Michaël Perrot, Aurélien BelletFederated LearningHeterogeneous Federated Learning

    Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStAL, F-59000 Lille, France · Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStALF-59000 Lille, France · PreMeDICaL team, Inria, Idesp, Inserm, Université de Montpellier

  10. 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

  11. FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion

    Mar 31, 2026Ningzhi Gao, Siquan Huang, Leyu Shi +1Federated LearningHeterogeneous Federated Learning

    South China University of Technology, Guangzhou, China

  12. Task-Centric Personalized Federated Fine-Tuning of Language Models

    Mar 30, 2026Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza +2Federated LearningFederated Fine-Tuning

    Universidade Estadual de Campinas · University of Cambridge · Flower Labs

  13. FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning

    Mar 30, 2026Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3Backdoor Attacks in Federated LearningFederated Learning

    Department of Computer and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada · Department of Computer Science, Khalifa University, Abu Dhabi, UAE · Artificial Intelligence & Cyber Systems Research Center, Department of CSM, Lebanese American University +2

  14. Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory

    Mar 30, 2026Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3Backdoor Attacks in Federated LearningFederated Learning

    Department of Computer and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada · Institut national de la recherche scientifique (INRS-EMT), Montréal, Canada · Department of Computer Science, Khalifa University, Abu Dhabi, UAE +1

  15. 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

  16. Key-Embedded Privacy for Decentralized AI in Biomedical Omics

    Mar 30, 2026Rongyu Zhang, Hongyu Dong, Gaole Dai +13Privacy-Preserving MLFederated Learning

    School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210023, China. · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, 999077, China. · State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, 100871, China. +8

  17. 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

  18. Pre-Deployment Complexity Estimation for Federated Perception Systems

    Mar 30, 2026KMA Solaiman, Shafkat Islam, Ruy de Oliveira +1Federated LearningEdge Computing

    University of Maryland Baltimore County, Baltimore, MD 21250, USA · Purdue University Northwest, Hammond, IN 46323, USA · Purdue University, West Lafayette, IN 47907, USA

  19. Neural Federated Learning for Livestock Growth Prediction

    Mar 30, 2026Shoujin Wang, Mingze Ni, Wei Liu +6Federated LearningTime Series Forecasting

    University of Technology Sydney, Sydney, Australia · AgriWebb, Sydney, Australia · Queensland University of Technology, Brisbane, Australia

  20. 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

  21. FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation

    Mar 30, 2026Ruiyang Wang, Rong Pan, Zhengan YaoPrivacy-Preserving Federated LearningFederated Learning

    School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China · School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China · Institute of Advanced Studies Hong Kong, Sun Yat-sen University, Hong Kong

  22. CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping

    Mar 29, 2026Alexandros S. Kalafatelis, Nikolaos Nomikos, Vasileios Nikolakakis +2Carbon-Aware ComputingFederated Learning

    Department of Ports Management and Shipping, National and Kapodistrian University of Athens, 34400 Euboea, Greece · Laskaridis Shipping Co. Ltd, 14562, Kifisia Greece

  23. BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities

    Mar 29, 2026Pranav M R, Jayant Chandwani, Ahmed M. Abdelmoniem +1Multimodal Federated LearningFederated Learning

    DaSH Lab, BITS Pilani, KK Birla Goa Campus, India · Queen Mary University of London, United Kingdom

  24. Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach

    Mar 27, 2026Abdelkrim Alahyane, Céline Comte, Matthieu JonckheereAsynchronous Federated LearningFederated Learning

    LAAS-SARA · EMINES, Mohammed VI Polytechnic University, Ben Guerir, Morocco · LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France +1

  25. Social Hippocampus Memory Learning

    Mar 26, 2026Liping Yi, Zhiming Zhao, Qinghua HuMemory-Augmented AgentsHeterogeneous Federated Learning

    College of Artificial Intelligence, Tianjin University.

  26. Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

    Mar 26, 2026Paul Shepherd, Tasos Dagiuklas, Bugra Alkan +1Trust-Aware Federated LearningIndustrial Internet of Things

    London South Bank University, London, UK. · Instituto de Telecomunica¸c˜oes, Aveiro, Portugal.

  27. Revealing the influence of participant failures on model quality in cross-silo Federated Learning

    Mar 26, 2026Fabian Stricker, David Bermbach, Christian ZirpinsFederated LearningFault-Tolerant Distributed Training

  28. Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation

    Mar 25, 2026Kenechi Omeke, Michael Mollel, Lei Zhang +2Federated LearningHierarchical Federated Learning

    James Watt School of Engineering, University of Glasgow, U.K.

  29. Optimizing Multilingual LLMs via Federated Learning: A Study of Client Language Composition

    Mar 25, 2026Aleix Sant, Jordi Luque, Carlos EscolanoFederated LearningFederated Fine-Tuning

    Scientific Research, Telefónica Innovación Digital · Universitat Politècnica de Catalunya

  30. 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.