Federated Learning

Ask about this topic

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. Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation

    Apr 27, 2026Huaicheng Li, Junhui Zhao, Haoyu Quan +1Communication-Efficient Distributed TrainingLow-Rank Adaptation

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China · School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

  2. FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

    Apr 27, 2026Yutong He, Zhengyang Huang, Jiahe Geng +1Communication-Efficient Distributed TrainingGradient Compression

    Peking University · Beihang University

  3. Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection

    Apr 25, 2026Prajwal Panth, Nishant NigamFederated Learning AggregationCommunication-Efficient Distributed Training

    School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India · School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, India

  4. Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

    Apr 25, 2026Emre Ardıç, Yakup GençNon-IID Federated LearningDifferentially Private Federated Learning

    Department of Computer Engineering, Gebze Technical University, Gebze, Kocaeli 41400, Türkiye

  5. A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning

    Apr 25, 2026Daan Rosendal, Ana OprescuFederated Learning

    Complex Cyber Infrastructure, University of Amsterdam, Amsterdam, The Netherlands

  6. Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning

    Apr 25, 2026Wugeng Zheng, Ziwen Kan, Katie Wang +2Multimodal Federated LearningTime Series Imputation

    University of Central Florida · Independent Researcher

  7. Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

    Apr 24, 2026Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan +1Federated Learning AggregationFederated Learning

    MBZUAI, UAE · Michigan State University, USA

  8. FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet

    Apr 24, 2026Thibault Pautrel, Florent Bouchard, Ammar Mian +1Federated Learning AggregationFederated Learning

    CentraleSup´elec, L2S, France · Universit´e Savoie Mont-Blanc, LISTIC, France

  9. Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

    Apr 24, 2026Hefeng Zhou, Xuan Liu, Sicheng Chen +7Cross-Modal LearningMultimodal Federated Learning

    Shanghai Jiao Tong University · Hohai University · Shenzhen Univ of Advanced Technology

  10. Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning

    Apr 23, 2026Amine BarrakFederated Learning AggregationCloud Computing

    Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA

  11. Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach

    Apr 23, 2026Guilin Deng, Silong Chen, Yuchuan Luo +6Membership Inference AttacksPrivacy Leakage in Language Models

    Colleague of Computer Science and Technology, National University of Defense Technology, Changsha, China · City University of Hong Kong, Hong Kong, China · Shenzhen University, Shenzhen, China

  12. FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

    Apr 22, 2026Sina Gholami, Abdulmoneam Ali, Tania Haghighi +2Noisy-Label LearningLearning with Noisy Labels

    University of North Carolina at Charlotte Charlotte, NC, USA

  13. Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

    Apr 22, 2026Beining Wu, Jun HuangMobile RoboticsFederated Continual Learning

    Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, 57006 SD

  14. Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

    Apr 22, 2026Jie Xu, Haaris Mehmood, Rogier Van Dalen +2Federated Learning AggregationNon-IID Federated Learning

    Samsung R&D Institute UK (SRUK) · Samsung AI Centre Cambridge

  15. Federated Learning over Blockchain-Enabled Cloud Infrastructure

    Apr 21, 2026Saloni Garg, Amit Sagtani, Kamal Kant HiranPrivacy-Preserving MLBlockchain-Based Federated Learning

    Adobe Research San Jose, USA · San Francisco State University San Francisco, USA · Sir Padampat Singhania University Udaipur, India

  16. Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints

    Apr 21, 2026Konstantinos Ziliaskopoulos, Alexander VinelPredict-Then-OptimizeLearning to Optimize

    Auburn University

  17. FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning

    Apr 21, 2026Abdulmoneam Ali, Ahmed ArafaClusteringNoisy-Label Learning

    Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA

  18. Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

    Apr 21, 2026Yuhan Hu, Xiaolei FangRUL EstimationPredictive Maintenance

    Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

  19. Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

    Apr 21, 2026Yi Zhao, Di Yuan, Tao Deng +2Wireless CommunicationsFederated Learning

    Department of Information Technology, Uppsala University, 75105 Uppsala, Sweden · School of Computer Science and Technology, Soochow University, Suzhou 215006, China · Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China +1

  20. FedSEA: Achieving Benefit of Parallelization in Federated Online Learning

    Apr 21, 2026Harekrushna Sahu, Pratik Jawanpuria, Pranay SharmaOnline Convex OptimizationNon-IID Federated Learning

    CMInDS, IIT Bombay, India

  21. Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

    Apr 21, 2026Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4Privacy-Preserving MLFederated Learning

  22. DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

    Apr 21, 2026Isaiah Thompson, Tanmay Sen, Ritwik BhattacharyaFederated LearningAnomaly Detection

    Department of Mathematical Sciences University of Texas at El Paso El Paso, TX 79968, USA · SQC & OR Unit Indian Statistical Institute Kolkata, 700118, India

  23. Federated Rule Ensemble Method in Medical Data

    Apr 20, 2026Ke Wan, Kensuke Tanioka, Toshio ShimokawaInterpretable MLPrivacy-Preserving ML

    Department of Medical Data Science Wakayama Medical University · Department of Biomedical Sciences and Informatics Doshisha University · Department of Biostatistics Wakayama Medical University

  24. Federation over Text: Insight Sharing for Multi-Agent Reasoning

    Apr 18, 2026Dixi Yao, Tahseen Rabbani, Manzil Zaheer +1Multi-Agent LLM SystemsMulti-Agent Reasoning

    University of Chicago · Google DeepMind

  25. FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction

    Apr 17, 2026Seerat Kaur, Sukhjit Singh Sehra, Dariush EbrahimiPrivacy-Preserving Language ModelsTraffic Flow Estimation

    Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, Canada

  26. SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models

    Apr 17, 2026Noor Islam S. Mohammad, Uluğ BayazıtLLM Hallucination MitigationPrivacy-Preserving Language Models

    Istanbul Technical University

  27. Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure

    Apr 17, 2026Stefan Behfar, Richard MortierCommunication-Efficient Distributed TrainingGroup Fairness

    Computer Lab, University of Cambridge, Cambridge, United Kingdom

  28. FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling

    Apr 17, 2026Xingyan Chen, Tian Du, Changqiao Xu +4Federated LearningHeterogeneous Federated Learning

    Beijing Key Laboratory of MEMS Technology and Device Reliability for Industrial Internet, the School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China · School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, P. R. China · Institute of Artificial Intelligence and the School of Computer Science, Beihang University, Beijing 100191, China, and with the SKLSDE +3

  29. Fed3D: Federated 3D Object Detection

    Apr 17, 2026Suyan Dai, Chenxi Liu, Fazeng Li +13D Object DetectionFederated Learning

    School of Automation Science and Enginnering, South China University of Technology, Guangzhou 510641, P.R.China. · State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China.

  30. Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics

    Apr 17, 2026Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1Hybrid Quantum-Classical MLHigh-Energy Physics

    qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE