Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Deep learning can achieve higher accuracy than traditional machine learning algorithms in
a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn …

High network communication cost for synchronizing gradients and parameters is the well-
known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary …

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Large-scale distributed optimization is of great importance in various applications. For data-
parallel based distributed learning, the inter-node gradient communication often becomes …

Building electric energy consumption forecasting is essential in establishing an energy
operation strategy for building energy management systems. Because of recent …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while kee** all the raw training …

Nowadays large-scale distributed machine learning systems have been deployed to support
various analytics and intelligence services in IT firms. To train a large dataset and derive the …

Serverless computing is becoming a promising paradigm for Distributed Deep Neural
Network (DDNN) training in the cloud, as it allows users to decompose complex model …