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Asynchronous Federated Optimization

arXiv 1903.03934 ↗First posted 10 Mar 2019cs.DC cs.LGcited by 267

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Abstract (arXiv)

Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergence to a global optimum, for both strongly convex and a restricted family of non-convex problems. Empirical results show that the proposed algorithm converges quickly and tolerates staleness in various applications.

Key facts. Cited by 267 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 48; 2022: 41; 2023: 52; 2024: 50; 2025: 47; 2026: 29.

267 papers.

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  1. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  2. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
  3. 2021Federated Learning: Opportunities and Challengescited by 84
  4. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  5. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 66
  6. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  7. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  8. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  9. 2021Anarchic Federated Learningcited by 42
  10. 2022A Survey on Heterogeneous Federated Learningcited by 42
  11. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  12. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
38 more
  1. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  2. 2022FedSynth: Gradient Compression via Synthetic Data in Federated Learningcited by 31
  3. 2021FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimizationcited by 29
  4. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  5. 2023Asynchronous Federated Continual Learningcited by 29
  6. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  7. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  8. 2023GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgettingcited by 27
  9. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
  10. 2021Resource-Efficient Federated Learningcited by 26
  11. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  12. 2024Asynchronous Local-SGD Training for Language Modelingcited by 26
  13. 2022A Survey on Federated Recommendation Systemscited by 25
  14. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  15. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  16. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  17. 2022Federated Learning in Satellite Constellationscited by 21
  18. 2024Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysiscited by 21
  19. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  20. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
  21. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  22. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  23. 2023MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligencecited by 20
  24. 2022AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregationcited by 19
  25. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  26. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  27. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  28. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  29. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  30. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  31. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  32. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  33. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
  34. 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
  35. 2021Asynchronous Federated Learning for Sensor Data with Concept Driftcited by 14
  36. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  37. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  38. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14

50 most cited of 267 All 267 (JSON)