Motivated by large-scale optimization problems arising in the context of machine learning,
there have been several advances in the study of asynchronous parallel and distributed …

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 …

In this article, we focus on solving a distributed convex optimization problem in a network,
where each agent has its own convex cost function and the goal is to minimize the sum of …

In this paper, we analyze the convergence of the alternating direction method of multipliers
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …

This paper presents a new class of gradient methods for distributed machine learning that
adaptively skip the gradient calculations to learn with reduced communication and …

Horizontal Federated learning (FL) handles multi-client data that share the same set of
features, and vertical FL trains a better predictor that combine all the features from different …

We introduce a novel federated learning framework allowing multiple parties having different
sets of attributes about the same user to jointly build models without exposing their raw data …

Abstract Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner,
suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods …

We introduce and analyze stochastic optimization methods where the input to each update
is perturbed by bounded noise. We show that this framework forms the basis of a unified …

High-quality large-scale scene rendering requires a scalable representation and accurate
camera poses. This research combines tile-based hybrid neural fields with parallel …