Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and send them to a central server for aggregation, thereby preventing leakage of sensitive raw data. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of each MD. To this purpose, we dynamically optimize MDs' transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions of the proposed CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize a Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.