MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
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Abstract (arXiv)
This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machine learning (ML) model using the rich data and computational resources of mobile clients without collecting their data in central systems. Conventional FL with client selection estimates the required time for an FL round from a given clients' computation power and throughput and determines a client set to reduce time consumption in FL rounds. However, it is difficult to obtain accurate resource information for all clients before the FL process is conducted because the available computation and communication resources change easily based on background computation tasks, background traffic, bottleneck links, etc. Consequently, the FL operator must select clients through exploration and exploitation processes. This paper proposes a multi-armed bandit (MAB)-based client selection method to solve the exploration and exploitation trade-off and reduce the time consumption for FL in mobile networks. The proposed method balances the selection of clients for which the amount of resources is uncertain and those known to have a large amount of resources. The simulation evaluation demonstrated that the proposed scheme requires less learning time than the conventional method in the resource fluctuating scenario.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 6; 2024: 0; 2025: 1.
11 papers.
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
- 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
- 2021Power Allocation for Wireless Federated Learning using Graph Neural Networkscited by 7
- 2022A Contribution-based Device Selection Scheme in Federated Learningcited by 7
- 2025Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Datacited by 4
- 2022Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selectioncited by 3
- 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3
- 2023Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participationcited by 3
- 2023Client Recruitment for Federated Learning in ICU Length of Stay Predictioncited by 0
- 2023Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devicescited by 0
- 2025Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data27 Mar 2025
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challenges20 Jul 2023
- 2023Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices14 Jul 2023
- 2023Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation19 May 2023
- 2023Client Recruitment for Federated Learning in ICU Length of Stay Prediction28 Apr 2023
- 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learning18 Apr 2023
- 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach18 Mar 2023
- 2022A Contribution-based Device Selection Scheme in Federated Learning10 Mar 2022
- 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT15 Feb 2022
- 2022Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection15 Jan 2022
- 2021Power Allocation for Wireless Federated Learning using Graph Neural Networks15 Nov 2021