While the emerging 6G networks are anticipated to meet the high-end service quality
demands of the mobile edge users in terms of data rate and delay satisfaction, new attack …

With the development of big data and artificial intelligence, distributed optimization has
emerged as an indispensable tool for solving large-scale problems. In particular, the multi …

The advent of distributed energy resources (DERs), such as distributed renewables, energy
storage, electric vehicles, and controllable loads, brings a significantly disruptive and …

Despite the many advantages of using deep neural networks over shallow networks in
various machine learning tasks, their effectiveness is compromised in a federated learning …

This study presents a novel approach for solving the economic dispatch (ED) problem in
groups of generating units communicating through a communication network. The …

This article reviews significant advances in networked signal and information processing
(SIP), which have enabled in the last 25 years extending decision making and inference …

This article considers the distributed nonconvex optimization problem of minimizing a global
cost function formed by a sum of local cost functions by using local information exchange …

In this paper, we consider a stochastic distributed nonconvex optimization problem with the
cost function being distributed over n agents having access only to zeroth-order (ZO) …

Inspired and underpinned by the idea of integral feedback, a distributed constant gain
algorithm is proposed for multiagent networks to solve convex optimization problems with …

We revisit the recent gradient tracking algorithm for distributed consensus optimization from
a control theoretic viewpoint. We show that the algorithm can be constructed by solving a …