mlsanityData to 14 Oct 2026

A Comprehensive Survey On Client Selections in Federated Learning

arXiv 2311.06801 ↗First posted 12 Nov 2023cs.LG cs.DCcited by 6

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Abstract (arXiv)

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critical factor for the performance of the overall system. In this survey, we provide a comprehensive overview of the state-of-the-art client selection techniques in FL, including their strengths and limitations, as well as the challenges and open issues that need to be addressed. We cover conventional selection techniques such as random selection where all or partial random of clients is used for the trained. We also cover performance-aware selections and as well as resource-aware selections for resource-constrained networks and heterogeneous networks. We also discuss the usage of client selection in model security enhancement. Lastly, we discuss open issues and challenges related to clients selection in dynamic constrained, and heterogeneous networks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 12 Nov 2023.

Builds on

18 of 45 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  7. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  9. 2018Generalized Byzantine-tolerant SGDcited by 95
  10. 2018Wireless Network Intelligence at the Edgecited by 92
  11. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  12. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
6 more
  1. 2019Active Federated Learningcited by 63
  2. 2020Communication-Efficient Federated Learning via Optimal Client Samplingcited by 39
  3. 2020Bandit-based Communication-Efficient Client Selection Strategies for Federated Learningcited by 29
  4. 2020Device Heterogeneity in Federated Learning: A Superquantile Approachcited by 21
  5. 2020Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learningcited by 16
  6. 2017Robust Decentralized Learning Using ADMM with Unreliable Agentscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networkscited by 1
  2. 2025S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learningcited by 0
  3. 2025XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicingcited by 0
  4. 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
  5. 2025FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networkscited by 0
  6. 2026FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Datacited by 0