Performance-Aware Client and Quantization Level Selection Algorithm for Fast Federated Learning

In federated learning (FL), which clients are selected and which quantization levels are chosen for the deep model parameters have significant impacts on the learning time as well as the learning accuracy. In this paper, we formulate a joint optimization problem on the client and quantization level selections. As a low complexity solution to the formulated problem, we develop a performance-aware client and quantization level selection (PA-CQLS) algorithm where the FL server estimates the individual round times of clients based on their computing power and channel quality, and determines the most appropriate clients and quantization levels accordingly. Simulation results show that PA-CQLS can reduce the round time by up to 70% compared to conventional algorithms.