The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Convolutional neural networks have gained a remarkable success in computer vision.
However, most usable network architectures are hand-crafted and usually require expertise …

Federated Learning (FL) aims to generate a global shared model via collaborating
decentralized clients with privacy considerations. Unlike standard distributed optimization …

This paper describes a third-generation parameter server framework for distributed machine
learning. This framework offers two relaxations to balance system performance and …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

The asynchronous parallel implementations of stochastic gradient (SG) have been broadly
used in solving deep neural network and received many successes in practice recently …