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Asynchronous Federated Learning on Heterogeneous Devices: A Survey

arXiv 2109.04269 ↗First posted 9 Sep 2021cs.DCcited by 93

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Abstract (arXiv)

Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Sep 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 15; 2023: 18; 2024: 25; 2025: 25; 2026: 5.

93 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  3. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 66
  4. 2022A Survey on Heterogeneous Federated Learningcited by 42
  5. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  6. 2023Momentum Benefits Non-IID Federated Learning Simply and Provablycited by 37
  7. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 36
  8. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  9. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  10. 2023Asynchronous Federated Continual Learningcited by 29
  11. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  12. 2022AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregationcited by 19
38 more
  1. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  2. 2022A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updatescited by 17
  3. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  4. 2021FedPrune: Towards Inclusive Federated Learningcited by 12
  5. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  6. 2022Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropoutcited by 12
  7. 2024Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learningcited by 12
  8. 2024FedFa: A Fully Asynchronous Training Paradigm for Federated Learningcited by 12
  9. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  10. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  11. 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
  12. 2022GitFL: Adaptive Asynchronous Federated Learning using Version Controlcited by 10
  13. 2023Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregationcited by 10
  14. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  15. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  16. 2023Differentially Private Federated Clustering over Non-IID Datacited by 7
  17. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  18. 2022Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Designcited by 6
  19. 2024HSViT: Horizontally Scalable Vision Transformercited by 6
  20. 2024Queuing dynamics of asynchronous Federated Learningcited by 6
  21. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  22. 2024Boosting Asynchronous Decentralized Learning with Model Fragmentationcited by 6
  23. 2022Semi-Synchronous Personalized Federated Learning over Mobile Edge Networkscited by 5
  24. 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
  25. 2023AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systemscited by 4
  26. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  27. 2025SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronizationcited by 4
  28. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  29. 2023Deep Equilibrium Models Meet Federated Learningcited by 3
  30. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  31. 2024I/O in Machine Learning Applications on HPC Systems: A 360-degree Surveycited by 3
  32. 2024FedAST: Federated Asynchronous Simultaneous Trainingcited by 3
  33. 2024Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimizationcited by 3
  34. 2024Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representationscited by 3
  35. 2024Quantized and Asynchronous Federated Learningcited by 3
  36. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
  37. 2025FedEL: Federated Elastic Learning for Heterogeneous Devicescited by 3
  38. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3

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