# Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning

arXiv: https://arxiv.org/abs/2509.13933 · HTML: https://mlsanity.com/paper/2509.13933/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Towards Energy-Aware Federated Learning on Battery-Powered Clients](https://mlsanity.com/paper/2208.04505/) (2022)

## Built on by (most cited first, 1 of 1)

- [Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks](https://mlsanity.com/paper/2601.01179/) (2026)

## Abstract (arXiv)

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.