# Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets

arXiv: https://arxiv.org/abs/2208.04322 · HTML: https://mlsanity.com/paper/2208.04322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks](https://mlsanity.com/paper/2011.12469/) (2020)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)

## Built on by (most cited first, 2 of 2)

- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach](https://mlsanity.com/paper/2409.11442/) (2024)

## 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.