Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization
problem with a composite objective containing smooth and nonsmooth terms. Specifically …

Decentralized optimization methods enable on-device training of machine learning models
without a central coordinator. In many scenarios communication between devices is energy …

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization
problems over a network (such as training a machine learning model). A key feature of GT is …

This article investigates the constrained nonsmooth distributed optimization problems
(DOPs) of general linear multiagent systems. Our problem involves the general linear …

Decentralized learning provides an effective framework to train machine learning models
with data distributed over arbitrary communication graphs. However, most existing …

The massive integration of uncertain distributed renewable energy resources into power
systems raises power imbalance concerns. Peer-to-peer (P2P) energy trading provides a …

This article studies the finite-time (FT) convergence of a fast primal–dual gradient dynamics
(PDGD), called FT-PDGD, for solving constrained optimization with general constraints and …

Distributed optimization methods with local updates have recently attracted a lot of attention
due to their potential to reduce the communication cost of distributed methods. In these …