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 …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …

Federated learning enables a large amount of edge computing devices to jointly learn a
model without data sharing. As a leading algorithm in this setting, Federated Averaging …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are distributed (unevenly) over an …

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

Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

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 …