In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

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

When training machine learning models using stochastic gradient descent (SGD) with a
large number of nodes or massive edge devices, the communication cost of synchronizing …

In this paper, we study the problem of distributed multi-agent optimization over a network,
where each agent possesses a local cost function that is smooth and strongly convex. The …

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 …

This paper considers the distributed optimization problem over a network, where the
objective is to optimize a global function formed by a sum of local functions, using only local …

In decentralized machine learning, workers compute model updates on their local data.
Because the workers only communicate with few neighbors without central coordination …

A new robust continuous-time optimization algorithm for distributed problems is presented
which guarantees fixed-time convergence. The algorithm is based on a Lyapunov function …

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 …

In this paper, we study decentralized online stochastic non-convex optimization over a
network of nodes. Integrating a technique called gradient tracking in decentralized …