Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware
technology to improve the spectrum and energy efficiency of wireless networks by artificially …

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key
enabler for envisioned 6G networks, for the purpose of improving the network capacity …

To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Over-the-air analog computation allows offloading computation to the wireless environment
through carefully constructed transmitted signals. In this paper, we design and implement …

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 …

The aim of this work is to propose a novel dynamic resource allocation strategy for adaptive
Federated Learning (FL), in the context of beyond 5G networks endowed with …

Over-the-air analog computation allows offloading computation to the wireless environment
through carefully constructed transmitted signals. In this paper, we design and implement …

In the rapidly developing field of wireless communication, the control of beams in
Reconfigurable Intelligent Surfaces (RISs) has emerged as a promising element beyond 5G …

This chapter presents a survey that focuses on the implementation of federated learning (FL)
techniques in sixth generation (6G) networks' physical layer (PHY) to meet the increasing …