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. SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding

    Mar 10, 2026Zheng Fang, Ziwei Niu, Ziyue Wang +5Surgical Video UnderstandingFederated Learning

    National University of Singapore, Singapore · Spectral AI Team, The Hong Kong University of Science and Technology (Guangzhou), China · Zhejiang University, China +1

  2. TASER: Task-Aware Spectral Energy Refine for Backdoor Suppression in UAV Swarms Decentralized Federated Learning

    Mar 10, 2026Sizhe Huang, Shujie YangBackdoor Attacks in Federated LearningFederated Learning

  3. Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G

    Mar 10, 2026Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +6Federated Learning6G Networks

    Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea. · Department of Artificial Intelligence, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea. · School of Computer Science, The University of Sydney, Australia +1

  4. Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training

    Mar 9, 2026Yiannis Papageorgiou, Yannis Thomas, Ramin Khalili +1Split Federated LearningFederated Learning

    Department of Informatics, Athens University of Economics and Business, Athens, Greece · Huawei Technologies Duesseldorf Gmbh, Munich, Germany

  5. 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

  6. Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor

    Mar 9, 2026Jiayu Huang, Xiaohu Wu, Tiantian He +1Federated Fine-TuningLow-Rank Adaptation

    Beijing University of Posts and Telecommunications, Beijing, China · Agency for Science, Technology and Research, Singapore, Singapore

  7. FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning

    Mar 9, 2026Peishen Yan, Yang Hua, Hao Wang +4Federated Fine-TuningFederated Learning Aggregation

    Shanghai Jiao Tong University · Queen’s University Belfast · Stevens Institute of Technology +2

  8. Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning

    Mar 8, 2026Jinshan Liu, Ken Li, Jiazhe Wei +2Federated Graph LearningFederated Learning

    School of Computer Science and Technology, Xi’an Jiaotong University · Ministry of Education Key Laboratory of Intelligent Networks and Network Security · Shaanxi Province Key Laboratory of Big Data Knowledge Engineering +1

  9. Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data

    Mar 8, 2026Pu Qiu, Chen Ouyang, Yongyang Xiong +3Error FeedbackFederated Learning

    School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, P.R China. · Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China. · Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.

  10. 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

  11. PromptGate Client Adaptive Vision Language Gating for Open Set Federated Active Learning

    Mar 7, 2026Adea Nesturi, David Dueñas Gaviria, Jiajun Zeng +1Active LearningFederated Learning

    University of Bonn, University Hospital Bonn, Clinic for Diagnostic and Interventional Radiology, 53127 Bonn, Germany

  12. FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring

    Mar 6, 2026Felix Walger, Mehdi Ejtehadi, Anke Schmeink +1Federated LearningGradient-Boosted Decision Trees

    Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, Germany · Spinal Cord Injury Artificial Intelligence (SCAI) Lab, ETH Zürich & Swiss Paraplegic Research (SPF), Nottwil, Switzerland

  13. FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification

    Mar 6, 2026Xin Xu, Binchang Ma, Zhixi Yu +1Federated Domain GeneralizationPerson Re-Identification

    School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065 China

  14. A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs

    Mar 6, 2026Yue Chang, Guangsen Lin, Jyun Jie Chuang +3Sepsis PredictionClinical Prediction

    Chengdu Medical College, Chengdu, Sichuan 610500, China · Kunming Medical University, Kunming, Yunnan 650500, China · National Yang Ming Chiao Tung University, Taipei, Taiwan 900, China +2

  15. First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints

    Mar 6, 2026Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1Stochastic OptimizationFederated Learning

    School of Electrical and Computer Engineering, Purdue University

  16. Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques

    Mar 5, 2026Yenan Wang, Carla Fabiana Chiasserini, Elad Michael SchillerFederated LearningDifferentially Private Federated Learning

    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden

  17. FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning

    Mar 5, 2026Junkang Liu, Fanhua Shang, Yuanyuan Liu +3Federated LearningAccelerated Gradient Methods

    School of Artificial Intelligence, Xi’an, Xidian University, China · College of Intelligence and Computing, Tianjin, Tianjin University, China · Medical College, Tianjin University; Peng Cheng Laboratory, Tianjin, China +1

  18. Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks

    Mar 5, 2026Ferhat Ozgur Catak, Murat Kuzlu, Jungwon Seo +1Federated LearningChannel Estimation

    University of Stavanger (UiS), Stavanger, Norway · Old Dominion University, Norfolk, VA, USA · University of York, York, UK

  19. Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study

    Mar 5, 2026Lu Yu, Zheng Chang, Ying-Chang LiangSplit Federated LearningFederated Learning

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China · Faculty of Information Technology, University of Jyväskylä, FIN-40014 Jyväskylä, Finland · Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China, 611731 Chengdu, China

  20. 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

  21. 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

  22. PTOPOFL: Topology-Guided Personalised Federated Learning with Persistent-Homology Client Descriptors

    Mar 4, 2026Kelly L Vomo-Donfack, Adryel Hoszu, Grégory Ginot +1Privacy-Preserving Federated LearningFederated Learning

    Université Sorbonne Paris Nord, LAGA, CNRS, UMR 7539, Laboratoire d’excellence Infibrex, F-93430 Villetaneuse, France · Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora (IHSM), Universidad de Málaga–Consejo Superior de Investigaciones Científicas, Málaga, Spain

  23. Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding

    Mar 4, 2026Patrick Wilhelm, Inese Yilmaz, Odej KaoClient Selection in Federated LearningFederated Learning

    BIFOLD Technische Universität Berlin, Germany · Technische Universität Berlin, Germany

  24. FedCova: Robust Federated Covariance Learning Against Noisy Labels

    Mar 4, 2026Xiangyu Zhong, Xiaojun Yuan, Ying-Jun Angela ZhangFederated LearningNoisy-Label Learning

    Department of Information Engineering, The Chinese University of Hong Kong · National Key Laboratory on Wireless Communications, University of Electronic Science and Technology of China

  25. LEA: Label Enumeration Attack in Vertical Federated Learning

    Mar 4, 2026Wenhao Jiang, Shaojing Fu, Yuchuan Luo +1Federated Learning

    College of Computer Science and Technology, National University of Defense Technology, Changsha, China

  26. 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

  27. Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality

    Mar 3, 2026Yenan Wang, Carla Fabiana Chiasserini, Elad Michael SchillerPrivacy-Preserving MLFederated Learning

    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden

  28. Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI

    Mar 3, 2026Edouard LansiauxQuantum Federated LearningPrivacy-Preserving Federated Learning

    Department of Emergency Medicine & STaR-AI Research Group, CHU de Lille, Université de Lille, Lille, France

  29. HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning

    Mar 3, 2026Zihao Peng, Nan Zou, Jiandian Zeng +4Federated LearningFederated Fine-Tuning

    Beijing Normal University · Zhengzhou University

  30. Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need

    Mar 3, 2026Xinran Zheng, Tara Javidi, Behrouz TouriStochastic Optimization ConvergenceStochastic Gradient Descent

    University of Illinois Urbana-Champaign · University of California San Diego