Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Data generated at the network edge can be processed locally by leveraging the paradigm of
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …

Recently, there has been a substantial interest in on-device Machine Learning (ML) models
to provide intelligence for the Internet of Things (IoT) applications such as image …

This article addresses the optimization problem of zeroth-order in a distributed setting,
where the gradient information is not available in the edge Internet of Things (IoT) clients …

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where
multiple devices collaboratively train a shared model without sharing their raw data with a …

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the
potential to foster numerous applications. However, it poses great challenges to train FL fast …

The continuous convergence of machine learning algorithms, 5G and beyond (5G+)
wireless communications, and artificial intelligence (AI) hardware implementation hastens …