Title: Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning Show Chinese title

Title: 基于Q学习的怀特指数的自适应客户端选择在无线联邦学习中

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. Show Chinese abstract

Abstract: 我们考虑无线联邦学习（FL）中的客户端选择问题，目标是减少达到一定学习精度所需的总时间。 由于服务器无法观察可能改变其计算和通信效率的客户端动态状态，我们将客户端选择建模为一个非静止多臂老虎机问题。 我们提出了一种可扩展且高效的方法，称为联邦Q-learning中的Whittle指数学习（WILF-Q），该方法使用Q-learning来自适应地学习和更新与每个客户端相关的近似Whittle指数，然后选择指数最高的客户端。 与现有方法相比，WILF-Q不需要显式了解客户端状态转移或数据分布，使其非常适合在实际FL设置中部署。 实验结果表明，WILF-Q在学习效率方面显著优于现有基线策略，为无线FL中的客户端选择提供了一种稳健且高效的方法。