Quality- and Availability-Based Device Scheduling and Resource Allocation for Federated Edge Learning

Abstract

To achieve an efficient federated edge learning (FEEL) system, the scheme of device scheduling and resource allocation should jointly perceive the device availability, wireless channel quality, and local gradient quality. The existing literature on FEEL rarely considers these three aspects simultaneously, so the schemes they proposed still have room for improving the efficiency of FEEL, which motivates our work. In this letter, by mathematically modeling the device availability, wireless channel quality, and gradient quality, and deriving the convergence bound for model training in the FEEL system, we formulate a joint device scheduling and resource allocation problem, aiming to improve the FEEL efficiency. The formulated problem is a challenging non-convex problem. By exploring its structural properties and utilizing the KKT conditions, we obtain an optimal solution in closed-form. The analytical results enable us to gain some important insights into how the device availability, wireless channel quality, and gradient quality affect device scheduling and resource allocation in the FEEL system.