To fully unleash the potential of edge devices, it is popular to cut a neural network into
multiple pieces and distribute them among available edge devices to perform inference …

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 …

As the popularity of deep learning in industry rapidly grows, efficient training of deep neural
networks (DNNs) becomes important. To train a DNN with a large amount of data, distributed …

As the number of edge devices with computing resources (eg, embedded GPUs, mobile
phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col …

Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …

We present PyDTNN, a framework for training deep neural networks (DNNs) on clusters of
computers that has been designed as a research-oriented tool with a low learning curve …

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 …

Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …

With the emergence of edge computing along with its local computation advantage over the
cloud, methods for distributed deep learning (DL) training on edge nodes have been …

With the prospering of mobile devices, the distributed learning approach, enabling model
training with decentralized data, has attracted great interest from researchers. However, the …