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math.OC2026
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov +3
Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy compo…
math.OC2024
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
Dmitry Bylinkin, Aleksandr Beznosikov
In recent years, as data and problem sizes have increased, distributed learning has become an essential tool for training high-performance models. However, the communication bottle…