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 …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …

This paper describes a novel algorithmic framework to minimize a finite-sum of functions
available over a network of nodes. The proposed framework, that we call GT-VR, is …

Many modern large-scale machine learning problems benefit from decentralized and
stochastic optimization. Recent works have shown that utilizing both decentralized …

In this paper, the economic dispatch problem (EDP) in smart grids is investigated over a
directed network, which concentrates on allocating the generation power among the …

In this paper, the distributed optimization problem for multi-agent systems under the
existence of cyber attacks is researched. Each agent has a private local cost function which …

Semi-decentralized federated learning blends the conventional device-to-server (D2S)
interaction structure of federated model training with localized device-to-device (D2D) …

In this paper, we study the problem of decentralized optimization to minimize a finite sum of
local convex cost functions over an undirected network. Compared with the existing works …

This study addresses the distributed nonconvex optimization problem for nonlinear multi-
agent systems over a weight-balanced and quasi-strongly connected graph. The purpose is …

Semi-decentralized federated learning blends the conventional device-to-server (D2S)
interaction structure of federated model training with localized device-to-device (D2D) …