  Large-scale saddle-point problems arise in such machine learning tasks as
GANs and linear models with affine constraints. In this paper, we study
distributed saddle-point problems (SPP) with strongly-convex-strongly-concave
smooth objectives that have different strong convexity and strong concavity
parameters of composite terms, which correspond to min and max variables, and
bilinear saddle-point part. We consider two types of first-order oracles:
deterministic (returns gradient) and stochastic (returns unbiased stochastic
gradient). Our method works in both cases and takes several consensus steps
between oracle calls.
