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Stochastic Client Selection for Federated Learning with Volatile Clients

arXiv 2011.08756 ↗First posted 17 Nov 2020cs.LG cs.DCcited by 38

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Abstract (arXiv)

Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a fraction of available clients are chosen to participate, and the selection decision might have a significant effect on the training efficiency, as well as the final model performance. In this paper, we investigate the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency. colorblackIntuitively, too much training failure might potentially reduce the training efficiency, while too much selection on clients with greater stability might introduce bias, thereby resulting in degradation of the training effectiveness. To tackle this tradeoff, we in this paper formulate the client selection problem under joint consideration of effective participation and fairness. Further, we propose E3CS, a stochastic client selection scheme to solve the problem, and we corroborate its effectiveness by conducting real data-based experiments. According to our experimental results, the proposed selection scheme is able to achieve up to 2x faster convergence to a fixed model accuracy while maintaining the same level of final model accuracy, compared with the state-of-the-art selection schemes.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 13; 2024: 6; 2025: 8; 2026: 3.

38 papers.

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  1. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  3. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  4. 2021Towards Fairness-Aware Federated Learningcited by 57
  5. 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
  6. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  7. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  8. 2023Knowledge Graph Self-Supervised Rationalization for Recommendationcited by 22
  9. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  10. 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
  11. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  12. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
26 more
  1. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  2. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  3. 2025Federated Retrieval Augmented Generation for Multi-Product Question Answeringcited by 10
  4. 2022ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Schemecited by 7
  5. 2023Improving Accelerated Federated Learning with Compression and Importance Samplingcited by 7
  6. 2023FedABC: Targeting Fair Competition in Personalized Federated Learningcited by 5
  7. 2023Efficient Node Selection in Private Personalized Decentralized Learningcited by 4
  8. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  9. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  10. 2022FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Trainingcited by 3
  11. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  12. 2024Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systemscited by 1
  13. 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
  14. 2025VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Securitycited by 1
  15. 2026FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devicescited by 1
  16. 2023Tackling Computational Heterogeneity in FL: A Few Theoretical Insightscited by 0
  17. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  18. 2024Tackling Non-IIDness in HAPS-Aided Federated Learningcited by 0
  19. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  20. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  21. 2025Drift-Aware Federated Learning: A Causal Perspectivecited by 0
  22. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  23. 2025Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approachcited by 0
  24. 2025FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networkscited by 0
  25. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0
  26. 2026Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learningcited by 0