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Large-scale saddle-point problems arise in such machine learning tasks asGANs and linear models with affine constraints. In this paper, we studydistributed saddle-point problems (SPP) with strongly-convex-strongly-concavesmooth objectives that have different strong convexity and strong concavityparameters of composite terms, which correspond to min and max variables, andbilinear saddle-point part. We consider two types of first-order oracles:deterministic (returns gradient) and stochastic (returns unbiased stochasticgradient). Our method works in both cases and takes several consensus stepsbetween oracle calls.

Decentralized Saddle-Point Problems with Different Constants of Strong Convexity and Strong Concavity