mlsanityData to 7 Oct 2026

Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

arXiv 2012.08009 ↗First posted 14 Dec 2020cs.LG cs.AIcited by 29

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Abstract (arXiv)

Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection has shown that selecting clients with higher local losses can improve error convergence speed. However, previously proposed biased selection strategies either require additional communication cost for evaluating the exact local loss or utilize stale local loss, which can even make the model diverge. In this paper, we present a bandit-based communication-efficient client selection strategy UCB-CS that achieves faster convergence with lower communication overhead. We also demonstrate how client selection can be used to improve fairness.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 9; 2024: 4; 2025: 1; 2026: 3.

29 papers.

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  1. 2021Federated Learning with Fair Averagingcited by 59
  2. 2021Towards Fairness-Aware Federated Learningcited by 57
  3. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  4. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 38
  5. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  6. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  7. 2022Study of the performance and scalability of federated learning for medical imaging with intermittent clientscited by 9
  8. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  9. 2021Sparsified Secure Aggregation for Privacy-Preserving Federated Learningcited by 8
  10. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
  11. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  12. 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 6
17 more
  1. 2023A Comprehensive Survey On Client Selections in Federated Learningcited by 6
  2. 2022Multi-Model Federated Learningcited by 4
  3. 2023Network Anomaly Detection Using Federated Learningcited by 4
  4. 2023Multi-dimensional Fair Federated Learningcited by 4
  5. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  6. 2022Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selectioncited by 3
  7. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  8. 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimizationcited by 1
  9. 2023Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variancecited by 1
  10. 2024Seamless Integration: Sampling Strategies in Federated Learning Systemscited by 1
  11. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  12. 2024FedEGG: Federated Learning with Explicit Global Guidancecited by 0
  13. 2024FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectificationcited by 0
  14. 2025Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learningcited by 0
  15. 2026Tackling Privacy Heterogeneity in Differentially Private Federated Learningcited by 0
  16. 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learningcited by 0
  17. 2026Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learningcited by 0