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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…
math.OC2024
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
Dmitry Bylinkin, Kirill Degtyarev, Aleksandr Beznosikov
Modern realities and trends in learning require more and more generalization ability of models, which leads to an increase in both models and training sample size. It is already di…