mlsanityData to 14 Oct 2026

A Survey on Participant Selection for Federated Learning in Mobile Networks

arXiv 2207.03681 ↗First posted 8 Jul 2022cs.DC cs.LGcited by 11

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Abstract (arXiv)

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID). Due to limited communication bandwidth and unstable availability of such devices in a mobile network, only a fraction of end devices (also referred to as the participants or clients in a FL process) can be selected in each round. Hence, it is of paramount importance to utilize an efficient participant selection scheme to maximize the performance of FL including final model accuracy and training time. In this paper, we provide a review of participant selection techniques for FL. First, we introduce FL and highlight the main challenges during participant selection. Then, we review the existing studies and categorize them based on their solutions. Finally, we provide some future directions on participant selection for FL based on our analysis of the state-of-the-art in this topic area.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

15 of 33 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  4. 2019A Survey on Distributed Machine Learningcited by 188
  5. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  6. 2019Reliable Federated Learning for Mobile Networkscited by 92
  7. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
  8. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  9. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  10. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
  11. 2021Anarchic Federated Learningcited by 42
  12. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
3 more
  1. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 39
  2. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  3. 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 2; 2025: 3.

11 papers.

  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  3. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  4. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  5. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  6. 2024Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Modelscited by 4
  7. 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
  8. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  9. 2023DCFL: Non-IID awareness Data Condensation aided Federated Learningcited by 0
  10. 2025FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networkscited by 0
  11. 2025FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networkscited by 0