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With the development of federated learning (FL), mobile devices (MDs) areable to train their local models with private data and sends them to a centralserver for aggregation, thereby preventing sensitive raw data leakage. In thispaper, we aim to improve the training performance of FL systems in the contextof wireless channels and stochastic energy arrivals of MDs. To this purpose, wedynamically optimize MDs' transmission power and training task scheduling. Wefirst model this dynamic programming problem as a constrained Markov decisionprocess (CMDP). Due to high dimensions rooted from our CMDP problem, we proposeonline stochastic learning methods to simplify the CMDP and design onlinealgorithms to obtain an efficient policy for all MDs. Since there are long-termconstraints in our CMDP, we utilize Lagrange multipliers approach to tacklethis issue. Furthermore, we prove the convergence of the proposed onlinestochastic learning algorithm. Numerical results indicate that the proposedalgorithms can achieve better performance than the benchmark algorithms.

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