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 …

Recently, artificial intelligence (AI) is undergoing a sustained success renaissance as it can
substantially improve networks' cognitive performance and intelligence, thereby contributing …

Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI
applications. Conventional approaches for training DNNs are generally implemented at …

The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

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 …

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its
core idea is still intriguing: Instead of sending all data and tasks from an end user's device to …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

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 …

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 …

Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …