  In this paper, we consider the decentralized, stochastic nonconvex
strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on
both primal and dual variables, wherein a network of $m$ computing agents
collaborate via peer-to-peer communications. We consider when the coupling
function is in expectation or finite-sum form and the double regularizers are
convex functions, applied separately to the primal and dual variables. Our
algorithmic framework introduces a Lagrangian multiplier to eliminate the
consensus constraint on the dual variable. Coupling this with
variance-reduction (VR) techniques, our proposed method, entitled VRLM, by a
single neighbor communication per iteration, is able to achieve an
$\mathcal{O}(\kappa^3\varepsilon^{-3})$ sample complexity under the general
stochastic setting, with either a big-batch or small-batch VR option, where
$\kappa$ is the condition number of the problem and $\varepsilon$ is the
desired solution accuracy. With a big-batch VR, we can additionally achieve
$\mathcal{O}(\kappa^2\varepsilon^{-2})$ communication complexity. Under the
special finite-sum setting, our method with a big-batch VR can achieve an
$\mathcal{O}(n + \sqrt{n} \kappa^2\varepsilon^{-2})$ sample complexity and
$\mathcal{O}(\kappa^2\varepsilon^{-2})$ communication complexity, where $n$ is
the number of components in the finite sum. All complexity results match the
best-known results achieved by a few existing methods for solving special cases
of the problem we consider. To the best of our knowledge, this is the first
work which provides convergence guarantees for NCSC minimax problems with
general convex nonsmooth regularizers applied to both the primal and dual
variables in the decentralized stochastic setting. Numerical experiments are
conducted on two machine learning problems. Our code is downloadable from
https://github.com/RPI-OPT/VRLM.
