Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
This addresses efficient resource allocation for federated learning services in wireless networks, but it appears incremental as it builds on existing client selection and payment optimization methods.
The paper tackles the problem of selecting clients for multiple federated learning services in wireless networks under monetary budget constraints, formalizing it as a non-cooperative Markov game and proposing a multi-agent hybrid deep reinforcement learning algorithm. Simulation results show that the algorithm significantly improves training performance, though no concrete numbers are provided.
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.