Aerial access networks (AANs) and mobile edge computing (MEC) have been considered
as key enablers of future networks. In this article, we investigate the application of MEC …

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

Recently, along with the rapid development of mobile communication technology, edge
computing theory and techniques have been attracting more and more attention from global …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as
key enablers of new applications, including: surveillance and monitoring, military, delivery of …

Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth
generation (6G) networks, which implies intelligence over the whole network from the core to …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

Recently, artificial intelligence (AI) is undergoing a sustained success renaissance as it can
substantially improve networks' cognitive performance and intelligence, thereby contributing …

Most conventional Federated Learning (FL) models are using a star network topology where
all users aggregate their local models at a single server (eg, a cloud server). That causes …

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …