mlsanityData to 14 Oct 2026

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

arXiv 2509.13933 ↗First posted 17 Sep 2025cs.LG cs.DCcited by 1

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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.

Key facts. Cited by 1 paper 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

5 of 15 references.

References · 5

  1. 2019Federated Machine Learning: Concept and Applicationscited by 412
  2. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  3. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  4. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  5. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networkscited by 0

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  1. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  2. 2024Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
  3. 2026Conflict-Aware Client Selection for Multi-Server Federated Learning
  4. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
  5. 2026Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
  6. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  7. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  8. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions