Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
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Abstract (arXiv)
We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 8 Aug 2022.
Builds on
8 of 16 references.
References · 8
- 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
- 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
- 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 54
- 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 54
- 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
- 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
- 2020Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networkscited by 11
- 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.