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. Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning

    Jan 10, 2026Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3Membership Inference AttacksFederated Fine-Tuning

    CISE, University of Florida, FL, USA · North Carolina State University, NC, USA · Washington State University, WA, USA +1

  2. Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

    Jan 5, 2026Mohammed Ayalew Belay, Adil Rasheed, Pierluigi Salvo RossiCommunication-Efficient Distributed TrainingIndustrial Anomaly Detection

    Department of Electronic Systems, Norwegian University of Science and Technology, 7034 Trondheim, Norway

  3. CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

    Jan 2, 2026Zhiheng Guo, Zhaoyang Liu, Zihan Cen +5Wireless CommunicationsFederated Learning

    School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China · Department of Computer Science, University of Exeter, Exeter EX4 4QJ, U.K. · Singapore University of Technology and Design, Singapore

  4. Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

    Dec 31, 2025Yuchao Hou, Zixuan Zhang, Jie Wang +9Adversarial TrainingRemote Sensing

    Shanxi Normal University, Taiyuan, China · Guizhou University, Guiyang, China · Shanxi Key Laboratory of Cryptography and Data Security, School of Computer Science and Artificial Intelligence, Shanxi Normal University, Taiyuan 030031, China +15

  5. Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg

    Dec 28, 2025Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail +2Federated Learning AggregationNon-IID Federated Learning

    Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan.

  6. FedVideoMAE: Efficient Federated Video Moderation with Differential Privacy and Secure Aggregation

    Dec 21, 2025Ziyuan Tao, Chuanzhi Xu, Sandaru Jayawardana +4Federated Learning AggregationVideo Content Moderation

    School of Computing, Macquarie University, Sydney, Australia · School of Computer Science, The University of Sydney, Sydney, Australia · School of Electrical and Computer Engineering, The University of Sydney, Sydney, Australia

  7. How (Mis)calibrated is your Federated CLIP and what to do about it?

    Dec 3, 2025Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3Vision-Language ModelsVLM Adaptation

    University of Trento, Italy · ´Ecole de technologie sup´erieure, QC, Canada · AMIAD, Pˆole Recherche, Palaiseau, France +2

  8. Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing

    Dec 3, 2025Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione, Kaibin HuangFederated Learning AggregationCommunication-Efficient Distributed Training

    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

  9. FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning

    Nov 30, 2025Haozhe WuMultimodal Federated LearningNon-IID Federated Learning

    School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China

  10. Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research

    Nov 25, 2025Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6Energy-Efficient MLFederated Learning

    Institute of Biomedical Engineering Karlsruhe Institute of Technology Fritz-Haber-Weg 1, Karlsruhe 76131 Baden-Württemberg, Germany · Department of Political Science Università Degli Studi Di Teramo Via Renato Balizarini 1, Teramo 64100 Italy · Department of Radiation Oncology University Medical Center Schleswig-Holstein Feldstrasse 21, Kiel 24105 Schleswig-Holstein, Germany +1

  11. Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling

    Nov 14, 2025Eunjeong Jeong, Nikolaos PappasEnergy-Efficient MLFederated Learning

    E. Jeong and N. Pappas are with the Department of Computer and Information Science, Linköping University, Sweden.

  12. Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning

    Nov 12, 2025Jian Wang, Hong Shen, Chan-Tong LamAdversarial AttacksBackdoor Attacks in Federated Learning

    Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · School of Cyberspace Security, Guangzhou University of Software, Guangzhou, China · School of Engineering and Technology, Central Queensland University, Australia

  13. A Byzantine-Robust Federated Learning with Nesterov Momentum

    Nov 4, 2025Lihan Xu, Xiaoyi Fan, Gang Wang +3Federated Learning AggregationByzantine-Robust Federated Learning

    School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China. · the Artificial Intelligence Research Institute and Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen 518172, China. · the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.

  14. 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

  15. Federated Computation of ROC and PR Curves

    Oct 6, 2025Xuefeng Xu, Graham CormodePrivacy-Preserving MLSecure Aggregation

    University of Warwick

  16. Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge

    Oct 6, 2025Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16Surgical Video UnderstandingCross-Dataset Generalization

  17. Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning

    Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Gradient Inversion AttacksAdversarial Attacks

    Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China

  18. Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection

    Sep 25, 2025Seohyeon Cha, Huancheng Chen, Haris VikaloFederated Continual LearningGradient Projection

    Department of Electrical and Computer Engineering The University of Texas at Austin

  19. FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning

    Sep 17, 2025Zhanting Zhou, Zeyu Ma, Ziqiang Zheng +1Federated Learning AggregationDomain Generalization

    University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China

  20. FedDAF: Federated Domain Adaptation Using Model Functional Distance

    Sep 15, 2025Mrinmay Sen, Sidhant Nair, C Krishna MohanFederated Learning AggregationDomain Adaptation

    Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Hyderabad, 502285, Telangana, India. · Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India.

  21. Practical Feasibility of Gradient Inversion Attacks in Federated Learning

    Aug 27, 2025Viktor Valadi, Lucas Beerens, Mattias Åkesson +4Gradient Inversion AttacksAdversarial Attacks

    Scaleout Systems Sweden · Recorded Future Sweden · AI Sweden Sweden +1

  22. 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

  23. 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

  24. FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs

    Aug 14, 2025Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3Communication-Efficient Distributed TrainingLLM Fine-Tuning

    Duke Kunshan University, Suzhou, China · Guangdong University of Technology, Guangzhou, China · Jade Bird Fire (JBF), China +1

  25. FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models

    Aug 7, 2025Chuan Li, Qianyi Zhao, Fengran Mo +1Communication-Efficient Distributed TrainingMedical LLMs

    East China Normal University, China · University of Montreal, Canada

  26. FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning

    Aug 6, 2025Tuan Nguyen, Sze Jue Yang, Khoa D. Doan +2Backdoor Attacks in Federated LearningFederated Learning

    College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA +1

  27. FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting

    Jul 24, 2025Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +3Asynchronous Federated LearningNon-IID Federated Learning

    Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China · Sun Yat-sen University, School of Computer Science and Engineering, Guangzhou 510275, China

  28. Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

    Jun 18, 2025Vincent Roca, Marc Tommasi, Paul Andrey +8Brain Age PredictionClinical Outcome Prediction

    Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France · Univ. Lille, Inria, F-59000 Lille, France · Univ. Montpellier, Inria, F-34090 Montpellier, France +3

  29. TRIP-Shapley: Propagation-Aware Contribution Attribution Framework for Peer-to-Peer Federated Learning

    May 29, 2025Honoka Anada, Tatsuya Kaneko, Yafei Ou +1Federated LearningShapley Value Attribution

    The University of Tokyo · Institute of Science Tokyo · RIKEN AIP

  30. 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