Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems

Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov, Leonid Berezin, Aram Avetisyan +1 more
1/13/2026
math.OCcs.LG

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Abstract

Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy composite under data similarity. By employing different sets of assumptions, we present several approaches to develop communication-efficient methods. An optimal algorithm is proposed for the convex case. The constructed theory is validated through a series of experiments across various problems.

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Cite this paper

@article{bylinkin2026accelerated,
  title  = {Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems},
  author = {Dmitry Bylinkin and Sergey Skorik and Dmitriy Bystrov and Leonid Berezin and Aram Avetisyan and Aleksandr Beznosikov},
  year   = {2026},
  eprint = {2601.08614},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.08614v1}
}

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