FedSD: A New Federated Learning Structure Used in Non-iid Data

Abstract

One of the most challenging problems in federated learning is the convergence speed problem caused by heterogeneity. We propose a novel structure called FedSD, a new method to accelerate the model convergence. We change the one-stage-cycle iteration structure to a 2-stage-cycle one to get the latest global gradient descent direction which can guide the model training direction. We instantiate algorithms using FedSD to improve the performance of experiments on several public datasets. Our empirical studies validate the excellent performance of FedSD.