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 …

The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to …

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 is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …

Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-
optimizing inter-dependent factors both at the per-job level and at the cluster-wide level …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

Deep learning models can take weeks to train on a single GPU-equipped machine,
necessitating scaling out DL training to a GPU-cluster. However, current distributed DL …

Machine learning (ML) workflows are extremely complex. The typical workflow consists of
distinct stages of user interaction, such as preprocessing, training, and tuning, that are …