mlsanityData to 14 Oct 2026

A Systematic Literature Review on Client Selection in Federated Learning

arXiv 2306.04862 ↗First posted 8 Jun 2023cs.LG cs.AIcited by 3

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Abstract (arXiv)

With the arising concerns of privacy within machine learning, federated learning (FL) was invented in 2017, in which the clients, such as mobile devices, train a model and send the update to the centralized server. Choosing clients randomly for FL can harm learning performance due to different reasons. Many studies have proposed approaches to address the challenges of client selection of FL. However, no systematic literature review (SLR) on this topic existed. This SLR investigates the state of the art of client selection in FL and answers the challenges, solutions, and metrics to evaluate the solutions. We systematically reviewed 47 primary studies. The main challenges found in client selection are heterogeneity, resource allocation, communication costs, and fairness. The client selection schemes aim to improve the original random selection algorithm by focusing on one or several of the aforementioned challenges. The most common metric used is testing accuracy versus communication rounds, as testing accuracy measures the successfulness of the learning and preferably in as few communication rounds as possible, as they are very expensive. Although several possible improvements can be made with the current state of client selection, the most beneficial ones are evaluating the impact of unsuccessful clients and gaining a more theoretical understanding of the impact of fairness in FL.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

18 of 62 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2019Fair Resource Allocation in Federated Learningcited by 394
  4. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  5. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  6. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  7. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  8. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 39
  9. 2020Bandit-based Communication-Efficient Client Selection Strategies for Federated Learningcited by 29
  10. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  11. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  12. 2022Shielding Federated Learning: Robust Aggregation with Adaptive Client Selectioncited by 18
6 more
  1. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  2. 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
  3. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  4. 2017Cognitive Biases in Software Engineering: A Systematic Mapping Studycited by 9
  5. 2021Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networkscited by 7
  6. 2021Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selectioncited by 7

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  2. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  3. 2025XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicingcited by 0