The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has
been actively applied to solve many problems, such as robust dimensionality reduction and …

We propose a novel single-loop decentralized algorithm, DGDA-VR, for solving the
stochastic nonconvex strongly-concave minimax problems over a connected network of …

We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT)
for nonconvex stochastic composite problems, with data heterogeneously distributed on …

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

Minimax problems have recently attracted a lot of research interests. A few efforts have been
made to solve decentralized nonconvex strongly-concave (NCSC) minimax-structured …

In this paper, we consider the decentralized, stochastic nonconvex strongly-concave
(NCSC) minimax problem with nonsmooth regularization terms on both primal and dual …

We focus on decentralized stochastic non-convex optimization, where $ n $ agents work
together to optimize a composite objective function which is a sum of a smooth term and a …

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

The long-term vision for 6G security is to implement AI-assisted frameworks that achieve
security automation without disrupting normal usage. Deep learning-based anomaly …

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 …