# MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks

arXiv: https://arxiv.org/abs/2009.13879 · HTML: https://mlsanity.com/paper/2009.13879/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT](https://mlsanity.com/paper/2202.07391/) (2022)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)
- [Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data](https://mlsanity.com/paper/2503.21213/) (2025)
- [Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection](https://mlsanity.com/paper/2201.05762/) (2022)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation](https://mlsanity.com/paper/2305.11633/) (2023)
- [Client Recruitment for Federated Learning in ICU Length of Stay Prediction](https://mlsanity.com/paper/2304.14663/) (2023)
- [Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices](https://mlsanity.com/paper/2307.07199/) (2023)

## 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.