Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918144028704768 |
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| author | Li, Qiyue Liu, Yingxin Qi, Hang Luo, Jieping Liu, Zhizhang Wu, Jingjin |
| author_facet | Li, Qiyue Liu, Yingxin Qi, Hang Luo, Jieping Liu, Zhizhang Wu, Jingjin |
| contents | We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which uses Q-learning to adaptively learn and update an approximated Whittle index associated with each client, and then selects the clients with the highest indices. Compared to existing approaches, WILF-Q does not require explicit knowledge of client state transitions or data distributions, making it well-suited for deployment in practical FL settings. Experiment results demonstrate that WILF-Q significantly outperforms existing baseline policies in terms of learning efficiency, providing a robust and efficient approach to client selection in wireless FL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning Li, Qiyue Liu, Yingxin Qi, Hang Luo, Jieping Liu, Zhizhang Wu, Jingjin Machine Learning Distributed, Parallel, and Cluster Computing We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which uses Q-learning to adaptively learn and update an approximated Whittle index associated with each client, and then selects the clients with the highest indices. Compared to existing approaches, WILF-Q does not require explicit knowledge of client state transitions or data distributions, making it well-suited for deployment in practical FL settings. Experiment results demonstrate that WILF-Q significantly outperforms existing baseline policies in terms of learning efficiency, providing a robust and efficient approach to client selection in wireless FL. |
| title | Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.13933 |