{"ID":2869834,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13933","arxiv_id":"2509.13933","title":"Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning","abstract":"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.","short_abstract":"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 c...","url_abs":"https://arxiv.org/abs/2509.13933","url_pdf":"https://arxiv.org/pdf/2509.13933v2","authors":"[\"Qiyue Li\",\"Yingxin Liu\",\"Hang Qi\",\"Jieping Luo\",\"Zhizhang Liu\",\"Jingjin Wu\"]","published":"2025-09-17T13:04:14Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\"]","methods":"[]","has_code":false}