mlsanityData to 14 Oct 2026

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

arXiv 2010.01243 ↗First posted 3 Oct 2020cs.LG cs.DC stat.MLcited by 198

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Abstract (arXiv)

Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 × faster and give 10% higher test accuracy than the baseline random selection.

Key facts. Cited by 198 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Oct 2020.

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Citing papers per year: 2021: 28; 2022: 35; 2023: 45; 2024: 42; 2025: 34; 2026: 14.

198 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 315
  2. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  3. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  4. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  5. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  6. 2021On Large-Cohort Training for Federated Learningcited by 74
  7. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  8. 2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 64
  9. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  10. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  11. 2021Towards Fairness-Aware Federated Learningcited by 58
  12. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 56
38 more
  1. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  2. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  3. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  4. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  5. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  6. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  7. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  8. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  9. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 36
  10. 2022Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learningcited by 33
  11. 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
  12. 2021Local Adaptivity in Federated Learning: Convergence and Consistencycited by 31
  13. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  14. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  15. 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
  16. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  17. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  18. 2021Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfercited by 23
  19. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  20. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  21. 2022A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updatescited by 17
  22. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  23. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  24. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  25. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  26. 2023Federated Learning with Regularized Client Participationcited by 14
  27. 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
  28. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  29. 2023Towards Interpretable Federated Learningcited by 11
  30. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 11
  31. 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availabilitycited by 10
  32. 2021Client Selection in Federated Learning based on Gradients Importancecited by 9
  33. 2022Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Frameworkcited by 9
  34. 2022When Do Curricula Work in Federated Learning?cited by 9
  35. 2023Client-specific Property Inference against Secure Aggregation in Federated Learningcited by 9
  36. 2023Federated Learning with Instance-Dependent Noisy Labelcited by 9
  37. 2024FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clusteringcited by 9
  38. 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9

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