mlsanityData to 7 Oct 2026

Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities

arXiv 2206.09446 ↗First posted 19 Jun 2022math.OC cs.DC cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 19 Jun 2022.

Builds on

35 of 53 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

8 papers.

  1. 2022Faster federated optimization under second-order similaritycited by 18
  2. 2023Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditionscited by 14
  3. 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysiscited by 11
  4. 2023Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalitiescited by 7
  5. 2024A Unified Theory of Stochastic Proximal Point Methods without Smoothnesscited by 4
  6. 2024Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learningcited by 2
  7. 2024Near-Optimal Distributed Minimax Optimization under the Second-Order Similaritycited by 0
  8. 2026Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problemscited by 0