Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …

Motivated by the increasing computational capacity of wireless user equipments (UEs), eg,
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …

The ongoing deployments of the Internet of Things (IoT)-based smart applications are
spurring the adoption of machine learning as a key technology enabler. To overcome the …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …

Wider coverage and a better solution to a latency reduction in 5G necessitate its
combination with multi-access edge computing technology. Decentralized deep learning …

The recent revival of AI is revolutionizing almost every branch of science and technology.
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …

Abstract 5 G is the fifth generation of cellular networks. It enables billions of connected
devices to gather and share information in real time; a key facilitator in Industrial Internet of …