Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

By enabling autonomous vehicles (AVs) to share data while driving, 5G vehicular
communications allow AVs to collaborate on solving common autonomous driving tasks …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

With growth in the number of smart devices and advancements in their hardware, in recent
years, data-driven machine learning techniques have drawn significant attention. However …

With 5G cellular systems being actively deployed worldwide, the research community has
started to explore novel technological advances for the subsequent generation, ie, 6G. It is …

Abstract 5G cellular networks are expected to be the key infrastructure to deliver the
emerging services. These services bring new requirements and challenges that obstruct the …

With the rapid growth in mobile computing, massive amounts of data and computing
resources are now located at the edge. To this end, Federated learning (FL) is becoming a …

Machine learning (ML) tasks are becoming ubiquitous in today's network applications.
Federated learning has emerged recently as a technique for training ML models at the …

Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …