Alternating direction method of multipliers (ADMM) is a widely used tool for machine
learning in distributed settings where a machine learning model is trained over distributed …

Modern big data applications tend to prefer a cluster computing approach as they are linked
to the distributed computing framework that serves users jobs as per demand. It performs …

Privacy preservation is addressed for decentralized optimization, where N agents
cooperatively minimize the sum of N convex functions private to these individual agents. In …

The presence of embedded electronics and communication capabilities as well as sensing
and control in smart devices has given rise to the novel concept of cyber-physical networks …

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

Alternating direction method of multiplier (ADMM) is a popular method used to design
distributed versions of a machine learning algorithm, whereby local computations are …

Distributed energy resources (DERs) are gaining prominence due to their advantages in
improving energy efficiency, reducing carbon emissions, and enhancing grid resilience …

Decentralized optimization is crucial for the design, deployment, and functionality of many
distributed systems. In this paper, we address the problem of privacy-preservation in …

This article addresses distributed optimization problems, in which a group of agents
cooperatively minimize the sum of their private objective functions via information …

An increasing number of distributed generators will penetrate into the distribution power
system in future smart grid, thus a centralized control strategy cannot effectively optimize the …