The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

With advancements of cloud technologies Multi-Access Edge Computing (MEC) emerged as
a remarkable edge-cloud technology to provide computing facilities to resource-restrained …

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based
deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL …

In order to describe the roadmap of current edge computing research activities, we first
address a brief overview of the most advanced edge computing surveys published in the last …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the
deployment of intelligent systems, driving the need for innovative data processing …

Multi-source domain adaptation has been intensively studied. The distribution shift in
features inherent to specific domains causes the negative transfer problem, degrading a …

The following study introduces FT-QNN, a federated and quantum teleportation–based
quantum neural network, utilized to optimize resource allocation for future wireless …

Federated learning (FL) safeguards user privacy by uploading gradients instead of raw data.
However, inference attacks can reconstruct raw data using gradients uploaded by users in …

Using big marine data to train deep learning models is not efficient, or sometimes even
possible, on local computers. In this paper, we show how distributed learning in the cloud …