8 papers
Decentralized Inexact Cubic Newton Method with Consensus Procedure
Artem Agafonov, Anton Novitskii, Alexander Rogozin +5
Distributed optimization is widely used in large-scale and privacy-preserving machine learning, where each agent stores a local objective and communicates only with its neighbors i…
Decentralized Optimization with Coupled Constraints
Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev +3
We consider the decentralized minimization of a separable objective , where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\math…
Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy +7
The objective of Vertical Federated Learning (VFL) is to collectively train a model using features available on different devices while sharing the same users. This paper focuses o…
Decentralized Optimization with Mixed Affine Constraints
Demyan Yarmoshik, Nhat Trung Nguyen, Alexander Rogozin +1
This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variable…
Dual Smoothing for Decentralized Optimization
Alexander Rogozin, Nhat Trung Nguyen, Hamed Azami Zenuzagh +1
Decentralized optimization is widely used in different fields of study such as distributed learning, signal processing, and various distributed control problems. In these types of…
Robustifying networks for flow problems against edge failure
Artyom Klyuchikov, Roland Hildebrand, Sergei Protasov +2
We consider the robust version of a multi-commodity network flow problem. The robustness is defined with respect to the deletion, or failure, of edges. While the flow problem itsel…