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 …

This paper proposes a wireless, goal-oriented, multi-user communication system assisted by
edge-computing, within the general framework of Edge Machine Learning (EML) …

Currently, the world experiences an unprecedentedly increasing generation of application
data, from sensor measurements to video streams, thanks to the extreme connectivity …

Goal-oriented communications represent an emerging paradigm for efficient and reliable
learning at the wireless edge, where only the information relevant for the specific learning …

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

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …

While high accuracy is of paramount importance for deep learning (DL) inference, serving
inference requests on time is equally critical but has not been carefully studied especially …

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 …

By extending computation capacity to the edge of wireless networks, edge computing has
the potential to enable computation-intensive and delay-sensitive applications in 5G and …

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 …