The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A
deep learning technique is widely used to analyze the potential value of these data due to its …

As a promising training model with Neural Network, distributed deep learning has been
widely applied in various scenarios, where clients and the cloud server work together only …

A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and
utilized to train machine learning models by IoT manufacturers to provide users with better …

Classical federated learning methods aggregate decentralized data from different devices
into a central location for efficient training. However, these approaches raise significant …

Distributed deep learning (DDL) naturally provides a privacy-preserving solution to enable
multiple parties to jointly learn a deep model without explicitly sharing the local datasets …

As a promising data-driven technology, deep learning has been widely employed in a
variety of Internet-of-Things (IoT) applications. Examples include automated navigation …

With the advent of the era of big data, deep learning has become a prevalent building block
in a variety of machine learning or data mining tasks, such as signal processing, network …

With the growth of intelligent Internet of Things (IoT) applications and services, deep neural
network (DNN) has become the core method to power and enable increased functionality in …

Deep Learning techniques have achieved remarkable results in many domains. Often,
training deep learning models requires large datasets, which may require sensitive …

Distributed training of deep neural networks faces three critical challenges: privacy
preservation, communication efficiency, and robustness to fault and adversarial behaviors …