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arXiv · 2601.08614

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

Dmitry Bylinkin·Sergey Skorik·Dmitriy Bystrov·Leonid Berezin·Aram Avetisyan·Aleksandr Beznosikov
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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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Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov, Leonid Berezin, Aram Avetisyan, Aleksandr Beznosikov. 2026-01-13. Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems. https://arxiv.org/abs/2601.08614

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