The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian, and military
applications. Wireless communication has always been one of the core technologies for …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly
train a neural network by communicating learning updates with an access point without …

Machine learning (ML) has attracted a great research interest for physical layer design
problems, such as channel estimation, thanks to its low complexity and robustness. Channel …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users'
local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless …

In this article, the deployment of federated learning (FL) is investigated in an energy
harvesting wireless network in which the base stations (BSs) employs massive multiple …

The proliferation of machine learning (ML) applications has lately witnessed a considerable
shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to …