In this paper, we introduce an accelerated distributed stochastic gradient method with
momentum for solving the distributed optimization problem, where a group of $ n $ agents …

We consider decentralized gradient-free optimization of minimizing Lipschitz continuous
functions that satisfy neither smoothness nor convexity assumption. We propose two novel …

In this paper, we consider solving the distributed optimization problem over a multi-agent
network under the communication restricted setting. We study a compressed decentralized …

Existing decentralized algorithms usually require knowledge of problem parameters for
updating local iterates. For example, the hyperparameters (such as learning rate) usually …

This paper focuses on minimizing the decentralized finite-sum optimization over a network,
where each pair of neighboring agents is associated with a nonlinear proximity constraint …

This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents
only communicate with their neighbors. We propose Decentralized Stochastic Gradient …

In this paper, we concentrate on decentralized optimization problems with nonconvex and
nonsmooth objective functions, especially on the decentralized training of nonsmooth neural …

Consider $ n $ agents connected over a network collaborate to minimize the average of their
local cost functions combined with a common nonsmooth function. This paper introduces a …