Abstract The Internet of Things (IoT) has revolutionized various sectors by enabling
seamless device interaction. However, the proliferation of IoT devices has also raised …

Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …

Quantum federated learning (QFL) is a novel framework that integrates the advantages of
classical federated learning (FL) with the computational power of quantum technologies …

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

With the emerging developments of the Metaverse, a virtual world where people can
interact, socialize, play, and conduct their business, it has become critical to ensure that the …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a
framework for training Quantum Machine Learning (QML) models via distributed networks …

Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated
learning (FL) over quantum networks, enabling collaborative quantum model training along …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …