Decentralization is a promising method of scaling up parallel machine learning systems. In
this paper, we provide a tight lower bound on the iteration complexity for such methods in a …

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 …

This paper studies a decentralized stochastic gradient tracking (DSGT) algorithm for non-
convex empirical risk minimization problems over a peer-to-peer network of nodes, which is …

In this paper, we focus on solving the decentralized optimization problem of minimizing the
sum of n objective functions over a multi-agent network. The agents are embedded in an …

The recent deployment of multi-agent networks has enabled the distributed solution of
learning problems, where agents cooperate to train a global model without sharing their …

Several interesting problems in multi-robot systems can be cast in the framework of
distributed optimization. Examples include multi-robot task allocation, vehicle routing, target …

While traditional federated learning (FL) typically focuses on a star topology where clients
are directly connected to a central server, real-world distributed systems often exhibit …

We study distributed multiagent optimization over graphs. We consider the minimization of
F+G subject to convex constraints, where F is the smooth strongly convex sum of the agent's …

In this paper, a push-sum based distributed delayed dual averaging algorithm (PS-DDDA) is
proposed to solve the distributed constrained optimization problem over the time-varying …

In this article, we come up with a novel Nesterov gradient and heavy-ball double accelerated
distributed synchronous optimization algorithm, called NHDA, and adopt a general …