Electric distribution grid operations typically rely on both centralized optimization and local
non-optimal control techniques. As an alternative, distribution system operational practices …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while kee** data localized. This framework faces several systems-oriented …

This article provides an overview of distributed gradient methods for solving convex machine
learning problems of the form minxRn (1/m) ΣR i= 1 fi (x) in a system consisting of mm …

Decentralized SGD is an emerging training method for deep learning known for its much
less (thus faster) communication per iteration, which relaxes the averaging step in parallel …

We study distributed optimization to minimize a sum of smooth and strongly-convex
functions. Recent work on this problem uses gradient tracking to achieve linear convergence …

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

Over-the-air computation (AirComp) is a promising technology converging communication
and computation over wireless networks, which can be particularly effective in model …

In this paper, we devise a communication-efficient decentralized algorithm, named as
communication-censored alternating direction method of multipliers (ADMM)(COCA), to …

Distributed optimization consists of multiple computation nodes working together to minimize
a common objective function through local computation iterations and network-constrained …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …