  This paper studies the stochastic nonconvex-strongly-concave minimax
optimization over a multi-agent network. We propose an efficient algorithm,
called Decentralized Recursive gradient descEnt Ascent Method (DREAM), which
achieves the best-known theoretical guarantee for finding the
$\epsilon$-stationary points. Concretely, it requires $\mathcal{O}(\min
(\kappa^3\epsilon^{-3},\kappa^2 \sqrt{N} \epsilon^{-2} ))$ stochastic
first-order oracle (SFO) calls and $\tilde{\mathcal{O}}(\kappa^2
\epsilon^{-2})$ communication rounds, where $\kappa$ is the condition number
and $N$ is the total number of individual functions. Our numerical experiments
also validate the superiority of DREAM over previous methods.
