We consider a model-distributed learning framework in which layers of a deep learning
model is distributed across multiple workers. To achieve consistent gradient updates during …

As the primary driver of intelligent mobile applications, deep neural networks (DNNs) have
gradually deployed to millions of mobile devices, producing massive latency-sensitive and …

With the increasing proliferation of Internet-of-Things (IoT) devices, it is a growing trend
toward training a deep neural network (DNN) model in pipeline parallelism across resource …

Deep neural network (DNN) training is generally performed by cloud computing platforms.
However, cloud-based training has several problems such as network bottleneck, server …

In the era of beyond 5G technology, it is expected that more and more applications can use
deep neural network (DNN) models for different purposes with minimum inference time …

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 …

This paper investigates the problem of performing distributed deep learning (DDL) to train
machine learning (ML) models at the edge with resource-constrained embedded devices …

Deep learning (DL) applications have attracted significant attention with the rapidly growing
demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL …

We address distributed machine learning in multitier (eg, mobile-edge-cloud) networks
where a heterogeneous set of nodes cooperate to perform a learning task. Due to the …

Deep Neural Networks (DNN) has been widely applied in big data-driven Internet of Things
(IoT) for excellent learning ability, while the black-box nature of DNN leads to uncertainty of …