Federated Learning Aggregation

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Overview

Federated learning aggregation studies how to combine knowledge from clients that keep their data local, so they can train useful models together. Methods range from FedAvg-style weighted averaging to client weighting, clustering, prototypes, and personalization; newer approaches also align low-rank or function-space updates when model parameters are not directly compatible. Robust filters and trust scores address poisoned or unreliable updates, while secure, asynchronous, and over-the-air aggregation target privacy, delays, and communication costs. No single strategy works best across settings: performance depends on client heterogeneity and participation, and practical designs must trade off accuracy, fairness, security, privacy, and efficiency.

Latest papers

CardsList

270 papers38 papers in the last four weeks, up 15% on the four weeks before. 0.2% of all new papers.

  1. Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization

    Oct 26, 2026Shreya Meel, Sennur Ulukus, Itzhak TamoPrivacy-Preserving Federated LearningFederated Learning Aggregation

    Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA · Department of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel

  2. SCAFFOLD under Hessian Similarity: Sharp Convergence Rates

    Oct 26, 2026Zhirayr Tovmasyan, Slavomír Hanzely, Egor Shulgin +2Federated LearningHeterogeneous Federated Learning

    King Abdullah University of Science and Technology (KAUST) · CISPA Helmholtz Center for Information Security · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

  3. Fed-MAPTrack: Federated Modality-aware Prompt Tuning for RGB-X Tracking

    Oct 26, 2026Xiangyu Liu, Yijie Shen, Yue Yin +3Multimodal Visual Object TrackingMultimodal Federated Learning

    Beijing Normal-Hong Kong Baptist University, 519087, Zhuhai, Guangdong, China · Zhejiang University, 310058, Hangzhou, Zhejiang, China · Peking University, 100871, Beijing, China +1

  4. Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems

    Oct 25, 2026Ting Xu, Henry LeungFederated LearningHeterogeneous Federated Learning

    Department of Biomedical Engineering, University of Calgary, Calgary, Canada · Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada

  5. Projection-Based Federated Learning on Compact Submanifolds with Geometry-Independent Differential Privacy

    Oct 24, 2026Thibault Pautrel, Florent Bouchard, Guillaume Ginolhac +2Differentially Private Federated LearningFederated Learning

    Université Paris-Saclay, CentraleSupélec, L2S, Gif-sur-Yvette, France · Université Paris-Saclay, CNRS, CentraleSupélec, L2S, Gif-sur-Yvette, France · Université Savoie Mont Blanc, LISTIC, Annecy, France +1

  6. FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging

    Oct 24, 2026Fatema Siddika, Abhijit Chunduru, Zilinghan Li +2Continual Learning for LLMsFederated Continual Learning

    Iowa State University, Ames, USA · Argonne National Laboratory, USA · University of Massachusetts Amherst, Amherst, USA

  7. Discount Yourself: Self-inclusive Freshness-weighted Gossip for Decentralised Learning under Client Outages

    Oct 20, 2026Qiyuan Wang, Ke Xiao, Christos AnagnostopoulosFederated Learning AggregationDecentralized Learning

    University of Glasgow

  8. Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging

    Oct 20, 2026Md Morshedul Islam, Hossain Shahriar, Alfredo CuzzocreaFederated LearningTraining Data Reconstruction Attacks

  9. LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning

    Oct 19, 2026Saheed A. Tijani, Frank Jiang, Ming LiuByzantine-Robust Federated LearningFederated Learning Aggregation

    Centre for Cyber Security Research and Innovation (CSRI), Deakin University, Geelong, Australia.

  10. CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning

    Oct 19, 2026Junhao Cheng, Yunming Liao, Hongli Xu +2Split Federated LearningFederated Learning

    School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China, 230027 · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China, 215123

  11. FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

    Oct 16, 2026Syed Md. Ahnaf Hasan, Md Akil Raihan Iftee, Sajib Mistry +2Prompt Learning for VLMsTest-Time Prompt Tuning

    Center for Computational & Data Sciences (CCDS), Independent University, Bangladesh · Rochester Institute of Technology, USA · Curtin University, Australia

  12. Initiating Removal from Operational Symptoms in Federated Vehicular Networks

    Oct 15, 2026Zihao Ding, Jun Huang, Qiang DuanFederated UnlearningFederated Learning

    Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA · Information Sciences and Technology Department, Pennsylvania State University, Abington, PA 19001, USA

  13. FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning

    Oct 15, 2026Reeyansh Shah, Maram Hasan, Subhasis Chaudhuri +1Federated UnlearningMachine Unlearning

    Indian Institute of Technology Bombay

  14. FUSE: Fairness-aware Update Screening against Byzantine Exploitation in qq-Fair Federated Learning

    Oct 14, 2026Tiantong Sun, Jinlin Liu, Xingru Wei +4Federated LearningFederated Learning Aggregation

    George Mason University · The University of Hong Kong · Huazhong University of Science and Technology +2

  15. Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs

    Oct 14, 2026Shuaijie Ma, Yongkang Hu, Yu Cheng +4Federated Continual LearningFederated Learning

    East China Normal University · Shanghai Innovation Institute

  16. The Federated Tumor Segmentation and Classification (FeTS++) Challenge

    Oct 13, 2026Wenyi Tang, Carla Pitarch-Abaigar, Siddhesh Thakur +5Medical Image SegmentationMedical Image Benchmarks

    Research Center for Federated Learning in Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. · Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. · Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA. +5

  17. FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation

    Oct 13, 2026Xi Chen, Chunyi Zhou, Rui Zeng +3Backdoor Attacks in Federated LearningLLM Backdoor Attacks

    Zhejiang University, Hangzhou, China

  18. Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning

    Oct 13, 2026Maria-Ioana-Andrea Ivan, Orhan Ermis, Ruxandra F. OlimidFederated Learning AggregationFederated Learning

    Department of Computer Science, University of Bucharest · Luxembourg Institute of Science and Technology (LIST), Luxembourg · Research Institute of the University of Bucharest (ICUB), Bucharest, Romania

  19. Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations

    Oct 12, 2026Rodrigo Tertulino, Ricardo Almeida, Laércio AlencarFederated LearningFederated Learning Aggregation

    Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN), Brazil

  20. FedPCD: Priority-Constrained Federated Aggregation

    Oct 9, 2026Yousef Irshaid, Dara Varam, Mohamed I. AlHajriFederated LearningFederated Learning Aggregation

    Department of Computer Science & Engineering, American University of Sharjah · Senseable City Laboratory, Massachusetts Institute of Technology

  21. Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing

    Oct 8, 2026Kai Li, Hanlin Cai, Zihao Liu +5Byzantine-Robust Federated LearningFederated Learning

    School of Computer Science and Digital Technologies, Aston University, Birmingham, UK · Centre for neXt Communications (CXC), Department of Engineering, University of Cambridge, CB3 0FA Cambridge, U.K. · School of Engineering, Edith Cowan University, Perth, WA 6027 +3

  22. PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks

    Oct 8, 2026Mustafa S. Aljumaily, Ali Ataeemh Allami, Mustafa N. Mnati +2Privacy-Preserving Federated LearningFederated Learning

    Daw Alfada Company, Amarah, Maysan, Iraq · University of Misan, Amarah, Maysan, Iraq · University of Southern Queensland, Toowoomba, QLD, Australia

  23. FedAlphaEdit: Null-Space-Aligned Merging for Collaborative Knowledge Editing

    Oct 8, 2026Sota Sugawara, Yukihiko OkadaKnowledge EditingLanguage Model Editing

    Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan · Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba, Tsukuba, Japan · Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan

  24. ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learning

    Oct 7, 2026Koffka KhanFederated LearningFederated Learning Aggregation

    Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies, St. Augustine, Trinidad and Tobago

  25. Efficient Secure Federated Learning via Information-Theoretically Secure Key Distribution: A Medical Imaging Case Study

    Oct 5, 2026Ivan Donà, Hans H. Brunner, Álvaro Troyano Olivas +3Federated Learning AggregationFederated Learning

    University of Trento Trento, Italy · Huawei Technologies Duesseldorf Munich, Germany · Universidad Politécnica de Madrid Madrid, Spain

  26. Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices

    Oct 5, 2026Hok Fong Wong, Hoi-To Wai, Chung-Yiu YauLogistic RegressionFederated Learning Aggregation

    Department of CSE, The Chinese University of Hong Kong, Hong Kong SAR · Department of SEEM, The Chinese University of Hong Kong, Hong Kong SAR · Department of ECE, University of Minnesota, Minnesota, USA

  27. Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning

    Oct 3, 2026Ilya Kuruzov, Dmitrii Vishovan, Kirill Novoselov +3Federated LearningFederated Learning Aggregation

    Innopolis University · Moscow Independent Research Institute of Artificial Intelligence · Lomonosov Moscow State University +3

  28. PoCoFL: POlicy-COmpliant Federated Learning

    Oct 2, 2026Dominik Roy George, Varesh Mishra, Aysajan AbidinFederated LearningPrivacy-Preserving Federated Learning

    COSIC, KU Leuven, Belgium

  29. Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

    Oct 2, 2026Abhijeet Parida, Zhifan Jiang, Pooneh Roshanitabrizi +6Medical Image SegmentationHeterogeneous Federated Learning

    Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, United States · Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain · CIBER-BBN, Instituto de Salud Carlos III, Madrid, Spain +2

  30. FedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection

    Oct 1, 2026Junkang LiuFederated Learning AggregationMemory-Efficient Fine-Tuning

    Tianjin University