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 …

We propose a dynamic resource allocation algorithm in the context of future wireless
networks endowed with edge computing, to enable accurate energy efficient classification …

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 …

In this paper, we address the problem of dynamic allocation of communication and
computation resources for Edge Machine Learning (EML) exploiting Multi-Access Edge …

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 …

In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate
inference at the wireless edge, in the context of 6G networks endowed with reconfigurable …

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 …

Model update compression is a widely used technique to alleviate the communication cost
in federated learning (FL). However, there is evidence indicating that the compression …

Bringing the success of modern machine learning (ML) techniques to mobile devices can
enable many new services and businesses, but also poses significant technical and …

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