The aim of this paper is to propose a resource allocation strategy for dynamic training and
inference of machine learning tasks at the edge of the wireless network, with the goal of …

Edge intelligence is an emerging network architecture that integrates sensing,
communication, computing components, and supports various machine learning …

Learning at the edge is a challenging task from several perspectives, since data must be
collected by end devices (eg sensors), possibly pre-processed (eg data compression), and …

The rapid development of artificial intelligence together with the powerful computation
capabilities of the advanced edge servers make it possible to deploy learning tasks at the …

With the prevalence of intelligent mobile applications, edge learning is emerging as a
promising technology for powering fast intelligence acquisition for edge devices from …

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 …

Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …

Federated Edge Learning (FEEL) involves the collaborative training of machine learning
models among edge devices, with the orchestration of a server in a wireless edge network …

Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network
to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A …

To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …