The impact that artificial intelligence (AI) has made across several industries in today's
society is clearly seen in applications ranging from medical diagnosis to customer service …

Billions of devices will compose the IoT system in the next few years, generating a huge
amount of data. We can use fog computing to process these data, considering that there is …

For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing
solutions to overcome key IoT challenges that include data processing, resource …

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 …

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 …

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed
intelligence of industrial automation thanks to its capability of distributed machine learning …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from
remote centers to the edge of the network. However, with its widespread deployment, new …

With the deep penetration of mobile devices, more and more mobile deep learning
applications have been widely used in daily life. However, since deep learning tasks are …

Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …