System Optimization of Federated Learning Networks with A Constrained Latency

TLDR: In this article, two bandwidth allocation schemes were proposed to maximize the number of active clients under the constraints of both latency and bandwidth in a federated learning network, where multiple mobile clients train their individual models with the help of one central server.

Abstract: This paper investigates a wireless federated learning (FL) network with limited communication bandwidth, where multiple mobile clients train their individual models with the help of one central server. We consider the practical communication scenarios, where the clients should complete the local computation and model upload within a defined latency. By jointly exploiting the dynamic characteristics of wireless channels and computational capability at the clients, we optimize the federated learning network by maximizing the number of active clients under the constraints of both latency and bandwidth. Specifically, we propose two bandwidth allocation (BA) schemes, where scheme I is based on the instantaneous channel state information (CSI), while scheme II employs the particle swarm optimization (PSO) method, based on the statistical CSI. Simulation results on the test accuracy and convergence rate are finally provided to demonstrate the advantages of the proposed optimization schemes for the considered FL network.