Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

With the increment of data sets and models sizes, distributed deep learning has been
proposed to accelerate training and improve the accuracy of DNN models. The parameter …

This article proposes a communication-efficient decentralized deep learning algorithm,
coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk …

Abstract Distributed training of Deep Learning models has been critical to many recent
successes in the field. Current standard methods primarily rely on synchronous centralized …

In recent years, a constant and fast information growing has characterized digital
applications in the majority of real-life scenarios. Thus, a new information asset, namely Big …

In recent years, artificial intelligence has achieved great success in many important
applications. Both novel machine learning algorithms (eg, deep neural networks), and their …

Deep learning with a large number of parametersrequires distributed training, where model
accuracy and runtimeare two important factors to be considered. However, there hasbeen …

Recent advances in decentralized deep learning algorithms have demonstrated cutting-
edge performance on various tasks with large pre-trained models. However a pivotal …

Abstract A Convolution Neural Network (CNN) is a popular tool in the domains of pattern
recognition and machine learning. The performance of KCNN (kernel-based convolutional …