5 papers
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Daniil Medyakov, Gleb Molodtsov, Grigoriy Evseev +2
Variational inequalities have gained significant attention in machine learning and optimization research. While stochastic methods for solving these problems typically assume indep…
Communication-Efficient Federated Learning with Adaptive Number of Participants
Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov +4
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framewo…
Bant: Byzantine Antidote via Trial Function and Trust Scores
Gleb Molodtsov, Daniil Medyakov, Sergey Skorik +6
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their st…
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Daniil Medyakov, Gleb Molodtsov, Savelii Chezhegov +2
Stochastic optimization algorithms are widely used for machine learning with large-scale data. However, their convergence often suffers from non-vanishing variance. Variance Reduct…
Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov
Variational inequalities as an effective tool for solving applied problems, including machine learning tasks, have been attracting more and more attention from researchers in recen…