The 5G network connecting billions of Internet of things (IoT) devices will make it possible to
harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

The implementation of artificial intelligence (AI) in wireless networks is becoming more and
more popular because of the growing number of mobile devices and the availability of huge …

By deploying machine-learning algorithms at the network edge, edge learning can leverage
the enormous real-time data generated by billions of mobile devices to train AI models …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and 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 …

By deploying machine learning algorithms at the network edge, edge learning recently
emerges as a promising framework to support intelligent mobile services. It effectively …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

This paper aims to establish a new optimization paradigm to efficiently execute distributed
learning tasks on wireless edge nodes with heterogeneous computing and communication …