The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

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 …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …