To build intelligent model learning in conventional architecture, the local data are required to
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …

This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …

The rapid advancement of wireless communication is forming a hyper-connected 5G
network in which billions of linked devices generate massive amounts of data. The traffic …

Abstract The Internet of Things (IoT) is an emerging technology that aims to connect
heterogeneous and constrained objects to each other and to the Internet. It has grown …

Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round
communications of multi-dimensional model updates between local participants and the …

The rapid growth of the Internet of Things (IoT) and its integration into various industries has
made it extremely challenging to guarantee IoT systems' dependability and quality, including …

The advancement of the sensing capabilities of end devices drives a variety of data-
intensive insights, yielding valuable information for modelling intelligent industrial …

The Internet of Things (IoT) has evolved from a novel technology to an integral part of our
everyday lives. It encompasses a multitude of heterogeneous devices that collect valuable …

The issue of multi-access services based on the rapidly expanding Internet affects
communication networks and creates congestion problems in buffers, which require effective …

With the increasing number of smart devices and the development of machine learning
technology, the value of users' personal data is becoming more and more important. Based …