mlsanityData to 7 Oct 2026

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

arXiv 2211.01549 ↗First posted 3 Nov 2022cs.LGcited by 62

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Abstract (arXiv)

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. Thus, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. To tackle the FL client heterogeneity problem, various client selection algorithms have been developed, showing promising performance improvement. In this paper, we systematically present recent advances in the emerging field of FL client selection and its challenges and research opportunities. We hope to facilitate practitioners in choosing the most suitable client selection mechanisms for their applications, as well as inspire researchers and newcomers to better understand this exciting research topic.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). Builds on 45 of its 98 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

45 of 98 references.

References · 45

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2020Adaptive Federated Optimizationcited by 1,036
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2018LEAF: A Benchmark for Federated Settingscited by 780
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  11. 2020Movement Pruning: Adaptive Sparsity by Fine-Tuningcited by 398
  12. 2019Fair Resource Allocation in Federated Learningcited by 389
33 more
  1. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  2. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  5. 2021A Field Guide to Federated Optimizationcited by 312
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  7. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  8. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  9. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  10. 2019A Survey on Distributed Machine Learningcited by 187
  11. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  12. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  13. 2020Optimal Client Sampling for Federated Learningcited by 123
  14. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  15. 2014Stochastic Optimization with Importance Samplingcited by 91
  16. 2019AI Benchmark: All About Deep Learning on Smartphones in 2019cited by 76
  17. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  18. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  19. 2020Fast-Convergent Federated Learningcited by 61
  20. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
  21. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  22. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  23. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  24. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
  25. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  26. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  27. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  28. 2020Optimal Importance Sampling for Federated Learningcited by 20
  29. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  30. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  31. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  32. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  33. 2021FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspectivecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 23; 2025: 23; 2026: 8.

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  1. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  2. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  3. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  4. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  5. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  6. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  7. 2025Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Modelscited by 4
  8. 2023Adaptive Federated Learning via New Entropy Approachcited by 3
  9. 2024CCFC: Bridging Federated Clustering and Contrastive Learningcited by 3
  10. 2024FedD2S: Personalized Data-Free Federated Knowledge Distillationcited by 3
  11. 2024CCFC++: Enhancing Federated Clustering through Feature Decorrelationcited by 3
  12. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
38 more
  1. 2025Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environmentscited by 3
  2. 2023FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learningcited by 2
  3. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  4. 2024Maverick-Aware Shapley Valuation for Client Selection in Federated Learningcited by 2
  5. 2024Data Measurements for Decentralized Data Marketscited by 2
  6. 2024SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learningcited by 2
  7. 2025FedFetch: Faster Federated Learning with Adaptive Downstream Prefetchingcited by 2
  8. 2025Collaborative Split Federated Learning with Parallel Training and Aggregationcited by 2
  9. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  10. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2
  11. 2025AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learningcited by 2
  12. 2023EcoLearn: Optimizing the Carbon Footprint of Federated Learningcited by 1
  13. 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learningcited by 1
  14. 2024Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offscited by 1
  15. 2024KnFu: Effective Knowledge Fusioncited by 1
  16. 2024IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirementscited by 1
  17. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  18. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  19. 2024Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budgetcited by 1
  20. 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
  21. 2025Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregationcited by 1
  22. 2026FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devicescited by 1
  23. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  24. 2023Utilizing Free Clients in Federated Learning for Focused Model Enhancementcited by 0
  25. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  26. 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehiclescited by 0
  27. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  28. 2024FedEGG: Federated Learning with Explicit Global Guidancecited by 0
  29. 2024Efficient Data Distribution Estimation for Accelerated Federated Learningcited by 0
  30. 2024PARDON: Privacy-Aware and Robust Federated Domain Generalizationcited by 0
  31. 2025S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learningcited by 0
  32. 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approachcited by 0
  33. 2025XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicingcited by 0
  34. 2025FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learningcited by 0
  35. 2025Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selectioncited by 0
  36. 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
  37. 2025Owen Sampling Accelerates Contribution Estimation in Federated Learningcited by 0
  38. 2025FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learningcited by 0

50 most cited of 62 All 62 (JSON)