In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Due to the advancements in cellular technologies and the dense deployment of cellular
infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

In this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …