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. UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment

    May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Multimodal Federated LearningFederated Learning Aggregation

    National Taiwan University · RIKEN-CCS · The University of Osaka +1

  2. Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

    May 8, 2026Hao Chen, Zavareh BozorgaslFederated Learning AggregationWireless Communications

    Signal, Communication, and Learning Lab (SCALE Lab), Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA

  3. Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA

    May 7, 2026Jinqian Chen, Chang Liu, Jihua ZhuFederated Learning AggregationLow-Rank Adaptation

    School of Software Engineering Xi’an Jiaotong University Xi’an, China 710000

  4. Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning

    May 7, 2026Myoungjun Kim, Sangwoo Park, Yoseob Han +1Differentially Private Federated LearningFederated Learning

    Myongji University · King’s College London · Soongsil University

  5. Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity

    May 6, 2026Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7Federated Learning AggregationLabel Distribution Learning

    School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · University of the Chinese Academy of Sciences, China +3

  6. Personalized Federated Learning for Gradient Alignment

    May 4, 2026Dongwon Kim, Gyuejeong LeeFederated Learning AggregationPAC-Bayesian Theory

    SAKAK Inc., Korea · Div. of Artificial Intelligence and Data Science, Korea Cyber University, Korea

  7. FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training

    May 4, 2026Yijiang Li, Emon Dey, Zilinghan Li +3Federated Learning AggregationHigh-Performance Computing

    Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA · Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA

  8. FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization

    May 1, 2026Zoe Fowler, Ghassan AlRegibFederated Learning AggregationCross-Domain Generalization

    Georgia Institute of Technology

  9. Federated Learning with Hypergradient-based Online Update of Aggregation Weights

    May 1, 2026Ayano Nakai-Kasai, Tadashi WadayamaFederated Learning AggregationHypergradient Optimization

    Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

  10. AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning

    Apr 30, 2026Zehui Tang, Yuchen Liu, Feihu HuangFederated Learning AggregationByzantine-Robust Federated Learning

    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China · MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.

  11. Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases

    Apr 29, 2026Gota MorishitaInverse Probability WeightingFederated Learning Aggregation

    Centre for Brain, Mind and Markets University of Melbourne Parkville, Victoria, Australia

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

    Apr 16, 2026He Yang, Dongyi Lv, Wei Xi +3Federated Learning AggregationByzantine-Robust Federated Learning

  18. Secure and Privacy-Preserving Vertical Federated Learning

    Apr 15, 2026Shan Jin, Sai Rahul Rachuri, Yizhen Wang +2Privacy-Preserving Federated LearningFederated Learning

    Visa Research

  19. Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation

    Apr 14, 2026Nafis Fuad Shahid, Maroof Ahmed, Md Akib Haider +3Multimodal Federated LearningMissing-Modality Learning

    Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur, Bangladesh

  20. The Impact of Federated Learning on Distributed Remote Sensing Archives

    Apr 13, 2026Anand Umashankar, Karam Tomotaki-Dawoud, Nicolai SchneiderFederated LearningNon-IID Federated Learning

    Technische Universität Berlin Berlin, Germany

  21. Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction

    Apr 10, 2026Zongming Cai, Jianhang Tang, Zhenyong Zhang +3Aspect-Based Sentiment AnalysisFederated Learning

    State Key Laboratory of Public Big Data, Guizhou University · College of Computer Science and Technology, Guizhou University · School of Information Engineering, Guangdong University of Technology +1

  22. Automating aggregation strategy selection in federated learning

    Apr 9, 2026Dian S. Y. Pang, Endrias Y. Ergetu, Eric Topham +1Federated Learning Aggregation

    Department of Computing, Imperial College London, UK · OctaiPipe, London, UK

  23. Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

    Apr 8, 2026Wenjing Wei, Farid Nait-Abdesselam, Alla JamminePrivacy-Preserving Federated LearningDifferentially Private Federated Learning

    Université Paris Cité, Paris, France

  24. Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach

    Apr 8, 2026Shengchao Chen, Guodong Long, Dikai Liu +1Time-Series Foundation ModelsTime Series Forecasting

    Australian AI Institute, University of Technology Sydney · Robotics Institute, University of Technology Sydney

  25. APPA: Adaptive Preference Pluralistic Alignment for Fair Federated RLHF of LLMs

    Apr 5, 2026Mahmoud Srewa, Tianyu Zhao, Salma ElmalakiLLM AlignmentPluralistic Alignment

    Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA 92697, USA

  26. 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

  27. 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

  28. FedSQ: Optimized Weight Averaging via Fixed Gating

    Apr 3, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +3Federated Learning AggregationFederated Fine-Tuning

    Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain · Openchip & Software Technologies Barcelona, Spain

  29. 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

  30. 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.