The mobile data traffic has been exponentially growing during the last several decades. This
was enabled by the densification of the network infrastructure in terms of increased cell …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

The conceptualisation of the sixth generation of mobile wireless networks (6G) has already
started with some potential disruptive technologies resonating as enablers for driving the …

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 …

Federated learning (FL) is an emerging distributed machine learning paradigm that protects
privacy and tackles the problem of isolated data islands. At present, there are two main …

Federated learning (FL) is an emerging distributed machine learning paradigm that protects
privacy and tackles the problem of isolated data islands. At present, there are two main …

The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to
realize federated learning (FL). However, sensitive information of user equipments (UEs) …

Federated learning (FL) is capable of performing large distributed machine learning tasks
across multiple edge users by periodically aggregating trained local parameters. To address …

Straggler effect is the main bottleneck in realizing federated learning (FL) in wireless
networks. This work proposes a novel user (UE) selection approach to mitigate this effect …