Joint Device Selection and Bandwidth Allocation for Layerwise Federated Learning

We consider the problem of reducing the learning latency of layerwise federated learning through joint device selection and bandwidth allocation. Specifically, we examine practical scenarios with heterogeneous devices with varying system parameters (e.g., CPU frequency, transmit power, etc.) and energy budgets. We formulate a long-term optimization problem, which is difficult to solve even with perfect channel state information. To address the issue, we employ Lyapunov theory to transform the problem into a series of online optimization problems, each of which can be efficiently solved using an alternating optimization-based method. Simulation results show that our scheduling scheme surpasses baseline schemes not only in terms of reducing the learning latency but also in reducing the energy deficit.