  Variational inequalities are an important tool, which includes minimization,
saddles, games, fixed-point problems. Modern large-scale and computationally
expensive practical applications make distributed methods for solving these
problems popular. Meanwhile, most distributed systems have a basic problem - a
communication bottleneck. There are various techniques to deal with it. In
particular, in this paper we consider a combination of two popular approaches:
compression and data similarity. We show that this synergy can be more
effective than each of the approaches separately in solving distributed smooth
strongly monotone variational inequalities. Experiments confirm the theoretical
conclusions.
