We study asynchronous federated learning mechanisms with nodes having potentially
different computational speeds. In such an environment, each node is allowed to work on …

We consider a decentralized convex unconstrained optimization problem, where the cost
function can be decomposed into a sum of strongly convex and smooth functions …

This work analyzes average-reward reinforcement learning with general parametrization.
Current state-of-the-art (SOTA) guarantees for this problem are either suboptimal or demand …

Abstract Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep
neural networks and generative models. Most theoretical results assume that it is possible to …

Byzantine-robust learning has emerged as a prominent fault-tolerant distributed machine
learning framework. However, most techniques focus on the static setting, wherein the …

The paper presents a review of the current state of subgradient and accelerated convex
optimization methods, including the cases with the presence of noise and access to various …

This paper examines a variety of classical optimization problems, including well-known
minimization tasks and more general variational inequalities. We consider a stochastic …

In cross-device federated learning (FL) with millions of mobile clients, only a small subset of
clients participate in training in every communication round, and Federated Averaging …

Variational inequalities as an effective tool for solving applied problems, including machine
learning tasks, have been attracting more and more attention from researchers in recent …

In this paper, we present a novel stochastic method for solving variational inequalities (VI) in
the context of Markovian noise. By leveraging Extragradient technique, we can productively …