Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

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 …

In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent
applications and services. As the network size becomes large, different users may generate …

In this letter, we propose a novel offloading learning approach to compromise energy
consumption and latency in a multi-tier network with mobile edge computing. In order to …

We consider a multi-user multi-server mobile edge computing (MEC) system, in which users
arrive on a network randomly over time and generate computation tasks, which will be …

The accelerating convergence of artificial intelligence (AI) and Internet of Things (IoT) has
sparked a recent wave of interest in Artificial Intelligence of Things (AIoT). By exploiting the …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for
privacy-preserving distributed learning over wireless networks. Air-FEEL allows" one-shot" …

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

The conventional model aggregation-based federated learning (FL) approach requires all
local models to have the same architecture, which fails to support practical scenarios with …