This article addresses a distributed optimization problem in a communication network where
nodes are active sporadically. Each active node applies some learning method to control its …

This paper addresses a distributed optimization problem in a large communication network,
where nodes are active sporadically. Each active node should properly control its action to …

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 …

This article is concerned with minimizing the average of cost functions over a network, in
which agents may communicate and exchange information with each other. We consider the …

We consider the distributed optimization problem where agents, each possessing a local
cost function, collaboratively minimize the average of the cost functions over a connected …

This paper considers a distributed stochastic non-convex optimization problem, where the
nodes in a network cooperatively minimize a sum of-smooth local cost functions with sparse …

In this article, the problem of distributed optimization with nonconvex objective functions is
studied by employing a network of agents. Each agent only has access to a noisy estimate …

This paper considers a distributed stochastic strongly convex optimization, where agents
over a network aim to cooperatively minimize the average of all agents' local cost functions …

We study gradient methods for solving distributed convex optimization problems over a
network when the communication bandwidth between the nodes is limited, and so …

The computational efficiency of the asynchronous stochastic gradient descent (ASGD)
against its synchronous version has been well documented in recent works. Unfortunately, it …