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

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Error feedback (EF), also known as error compensation, is an immensely popular
convergence stabilization mechanism in the context of distributed training of supervised …

It is well understood that client-master communication can be a primary bottleneck in
Federated Learning. In this work, we address this issue with a novel client subsampling …

We present a unified framework for analyzing local SGD methods in the convex and strongly
convex regimes for distributed/federated training of supervised machine learning models …

We consider distributed optimization over several devices, each sending incremental model
updates to a central server. This setting is considered, for instance, in federated learning …

Modern deep learning models are often trained in parallel over a collection of distributed
machines to reduce training time. In such settings, communication of model updates among …

In this paper we introduce a unified analysis of a large family of variants of proximal
stochastic gradient descent (SGD) which so far have required different intuitions …

Modern large-scale machine learning applications require stochastic optimization
algorithms to be implemented on distributed compute systems. A key bottleneck of such …

In distributed or federated optimization and learning, communication between the different
computing units is often the bottleneck and gradient compression is widely used to reduce …