Deep Reinforcement Learning-Empowered Federated Learning for Wireless Clients with Energy and Bandwidth Constraints

Federated learning (FL) is a privacy-preserving machine learning (ML) approach that allows clients to perform joint model training without needing to share their individual training data with the central server. When deploying FL for wireless clients with bandwidth and energy constraints, efficient utilization of communication resources is a significant challenge. In this paper, we propose a Deep Reinforcement Learning (DRL)-empowered FL framework for wireless clients that utilizes a Deep Deterministic Policy Gradient (DDPG) agent at the central server to allocate communication bandwidth to each client. The DRL aims to reduce each clients’ transmission energy by considering their respective channels to the server. Model partitioning is used to control bandwidth utilization. The proposed method trades-off the amount of information transmitted by each client and the accuracy of the central model. Simulation results demonstrate that our proposed framework outperforms baseline FL methods operating under the same constraints.