The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

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

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

As the 5G communication networks are being widely deployed worldwide, both industry and
academia have started to move beyond 5G and explore 6G communications. It is generally …

Critical infrastructure systems are vital to underpin the functioning of a society and economy.
Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the
confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages …