The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

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

Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

Federated learning (FL) coordinates with numerous heterogeneous devices to
collaboratively train a shared model while preserving user privacy. Despite its multiple …

Inspired by recent work of Islamov et al (2021), we propose a family of Federated Newton
Learn (FedNL) methods, which we believe is a marked step in the direction of making …

To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …

Abstract Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent
algorithms for solving min-max optimization and variational inequalities problems (VIP) …

In this work we focus our attention on distributed optimization problems in the context where
the communication time between the server and the workers is non-negligible. We obtain …

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in
collaborative and federated learning. However, many fruitful directions, such as the usage of …

Motivated by recent increased interest in optimization algorithms for non-convex
optimization in application to training deep neural networks and other optimization problems …