Distributed edge intelligence is a disruptive research area that enables the execution of
machine learning and deep learning (ML/DL) algorithms close to where data are generated …

Processing data generated at high volume and speed from the Internet of Things, smart
cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data …

Deploying machine learning on edge devices is becoming increasingly important, driven by
new applications such as smart homes, smart cities, and autonomous vehicles …

The explosion of data volumes generated by an increasing number of applications is
strongly impacting the evolution of distributed digital infrastructures for data analytics and …

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 …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Deep Neural Networks (DNN) have been widely used in a large number of application
scenarios. However, DNN models are generally both computation-intensive and memory …

In edge computing, most procedures, including data collection, data processing, and service
provision, are handled at edge nodes and not in the central cloud. This decreases the …

Deep learning is now present in a wide range of services and applications, replacing and
complementing other machine learning algorithms. Performing training and inference of …

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …