Advances in communication technologies and computational capabilities of Internet of
Things (IoT) devices enable a range of complex applications that require ever increasing …

Recently, mobile and embedded devices worldwide generate a massive amount of data at
the network edge. To efficiently exploit the data from distributed devices, we concentrate on …

Industrial Internet of Things (IIoT) applications can benefit from leveraging edge computing.
For example, applications underpinned by deep neural networks (DNN) models can be …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

With recent advancements in deep neural networks (DNNs), we are able to solve
traditionally challenging problems. Since DNNs are compute intensive, consumers, to …

Deep Neural Networks (DNNs) have been widely used in everyday life owing to their
impressive performance in complex machine learning tasks. The performance however …

Recent advances in deep neural networks have substantially improved the accuracy and
speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference …

Abstract Internet of Things (IoT) edge intelligence has emerged by optimizing the deep
learning (DL) models deployed on resource‐constraint devices for quick decision‐making …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

The emergence of the Internet of Things (IoT) has led to a remarkable increase in the
volume of data generated at the network edge. In order to support real-time smart IoT …