Asynchronous Federated Learning

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Overview

Asynchronous federated learning studies how clients can collaboratively train a model while contributing updates at different times, keeping their raw data local. Common approaches include staleness-aware weighting and momentum, buffered or push-sum aggregation, and distillation to reuse information from delayed or heterogeneous updates; systems methods also account for network queues, client availability, and communication limits. Work evaluates these methods in settings such as non-IID vision and graph benchmarks, medical imaging, language-model fine-tuning, and unreliable wireless networks. The central tension is gaining speed and resilience to stragglers without letting stale, biased, or malicious updates undermine convergence, fairness, or privacy.

Latest papers

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25 papers3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

  1. 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

  2. Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

    Oct 2, 2026Sreejeet Maity, Aritra MitraFederated RLByzantine-Robust Federated Learning

    Department of Electrical and Computer Engineering North Carolina State University, Raleigh

  3. Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

    Oct 1, 2026Emilio Paolini, Andrea Pinto, Flavio Esposito +1Federated Learning AggregationAsynchronous Federated Learning

    Tecip Institute, Scuola Superiore Sant’Anna, Pisa, Italy · Computer Science Department, Saint Louis University, St. Louis, USA

  4. QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles

    Sep 17, 2026Baran Can Gül, Mert Nakıp, Nasser Jazdi +1Multimodal Federated LearningAsynchronous Federated Learning

    Institute of Industrial Automation and Software Engineering, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany · Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (IITIS PAN), 44-100 Gliwice, Poland · Ideatrum Ltd. Sti., 35560, İzmir, Türkiye

  5. Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

    Sep 14, 2026Zhen Zhong, Shini Yang, Liesheng WeiFederated Learning AggregationAsynchronous Federated Learning

    Georgetown University, Washington, D.C., USA · LinkedIn, CA, USA · Shanghai Ocean University, Shanghai, China

  6. HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

    Sep 9, 2026Osama Abu Hamdan, Rabin Pandey, Hao Che +2Asynchronous Federated LearningFederated Learning

    University of Texas at Arlington · Missouri University of Science and Technology · Meta Platforms, Inc.

  7. NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces

    Sep 9, 2026Daniel Adu Worae, Aarthy NagarajanElectroencephalographyBrain-Computer Interfaces

    Department of Computer Science and Engineering, University of Notre Dame, 307 Fitzpatrick Hall, Notre Dame, IN 46556, USA · Department of Computer Science and Engineering and the Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, USA

  8. FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning

    Sep 7, 2026Prashant Bajpai, Divya Saxena, Philippe Lalanda +1Federated Learning AggregationAsynchronous Federated Learning

    School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India · Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, India · Laboratoire d’Informatique de Grenoble (LIG), University of Grenoble Alpes, Grenoble, France

  9. Robust Federated Learning Under Real-World Client Churn

    Jul 8, 2026Dhruv Garg, Neha Lakhani, Debopam Sanyal +3Federated Learning AggregationCommunication-Efficient Distributed Training

    Georgia Tech USA · Cisco Research USA

  10. Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

    Jun 9, 2026Chanuka A. S. Hewa Kaluannakkage, Rajkumar BuyyaFederated Learning AggregationAsynchronous Federated Learning

    Quantum Cloud and Distributed Systems (qCLOUDS) Lab School of Computing and Information Systems The University of Melbourne, Australia

  11. AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments

    Jun 6, 2026Yan Wang, Ziyi Gao, Rui WangLLM Fine-TuningAsynchronous Federated Learning

    Department of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

  12. Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries

    Jun 1, 2026Hina Dixit, Punit Kumar, Irene Tenison +1Privacy-Preserving Language ModelsCommunication-Efficient Distributed Training

    Decompute Inc., Cupertino

  13. On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach

    May 24, 2026Jiahui Bai, Hai Dong, A. K. QinMulti-Agent CoordinationFederated Learning Aggregation

    School of Computer Technologies, RMIT University Melbourne, VIC, Australia · School of Science, Computing and Engineering Technologies, Swinburne University of Technology Hawthorn, VIC, Australia

  14. 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

  15. Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

    Apr 29, 2026Zhaoyuan Cai, Xinglin ZhangFederated UnlearningMedical Imaging

    School of Computer Science and Engineering South China University of Technology Guangzhou, China

  16. Asynchronous Probability Ensembling for Federated Disaster Detection

    Apr 15, 2026Emanuel Teixeira Martins, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +3Federated LearningAsynchronous Federated Learning

    Federal University of Espirito Santo (UFES), Vitória-ES, Brazil · Federal University of Rio Grande do Norte (UFRN), Natal-RN, Brazil · University of Minho (UMinho), Braga, Portugal

  17. 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

  18. Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach

    Mar 27, 2026Abdelkrim Alahyane, Céline Comte, Matthieu JonckheereAsynchronous Federated LearningFederated Learning

    LAAS-SARA · EMINES, Mohammed VI Polytechnic University, Ben Guerir, Morocco · LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France +1

  19. DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning

    Mar 19, 2026Yizhou Han, Di Wu, Blesson VargheseFederated LearningFederated Continual Learning

    School of Computer Science, University of St Andrews, UK

  20. Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation

    Mar 9, 2026Patrick Wilhelm, Odej KaoFederated Learning AggregationAsynchronous Federated Learning

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

  21. 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

  22. FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning

    Feb 17, 2026Chaoyi Lu, Yiding Sun, Zhichuan Yang +3Asynchronous Federated LearningFederated Learning Aggregation

  23. Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

    Nov 1, 2025Baris Askin, Holger R. Roth, Zhenyu Sun +3Asynchronous Federated LearningFederated Learning Aggregation

    Carnegie Mellon University · NVIDIA · Northwestern University

  24. 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

  25. Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces

    Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning AggregationFederated Continual Learning

    University of Oxford, Oxford, United Kingdom · University of Bristol, Bristol, United Kingdom