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 …

The Internet of Things (IoT) offers unprecedented opportunities to access anything from
anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount …

With the advent of 6G technology, the proliferation of interconnected devices necessitates a
robust, fully connected intelligence network. Federated Learning (FL) stands as a key …

Nowadays, an ambitious target of the next-generation networks is to develop intelligent
overarching space–air–ground–aqua computing systems, in order to provide a smart …

This paper explores the practical implementation and performance analysis of distributed
learning (DL) frameworks on various client platforms, responding to the dynamic landscape …

The wireless communication systems, particularly the vehicle to everything (V2X) wireless
networks, take advantage of machine learning (ML) to improve and overcome the issues …

With the advent of 6G technology, the proliferation of interconnected devices necessitates a
robust, fully connected intelligence network. Federated Learning (FL) stands as a key …

Graph neural networks (GNNs) have been used for optimizing resource allocation due to
their potential in scalability and size generalizability. To facilitate their application to large …

With recent advancements in Internet of Things (IoT), Machine Learning (ML), and wireless
communication networks, eg, 6G, there has been an increasing interest in Intelligent …

Nowadays, there is a remarkable interest in Edge Intelligent Computing systems as key
technology for next-generation intelligent applications. The emerging paradigms of …