Edge Intelligence for Beyond-5G through Federated Learning

Abstract

The computational capabilities of mobile devices have been advancing at a rapid pace in recent times, leading to a growing interest in deploying machine learning applications on such devices. In parallel, Mobile Edge Computing (MEC) has gained traction as a potential enabler for many applications in 5G and Beyond-5G networks, paving the path for making edge devices more intelligent through distributed learning strategies. In this article, we overview the application of federated learning (FL), a novel privacy-preserving distributed learning strategy, within the context of MEC. Minimizing communications latency involved in FL tasks as well as optimizing FL tasks for resource-constrained Internet of Things (IoT) devices are investigated.