3 citations · 3 across the 3 of their papers we have counts for
20 papers · 1 filter
Tighter Performance Theory of FedExProx
Wojciech Anyszka, Kaja Gruntkowska, Alexander Tyurin +1
We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal algorithms via extrapolation. In the proc…
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
Artavazd Maranjyan, Omar Shaikh Omar, Peter Richtárik
We investigate the problem of minimizing the expectation of smooth nonconvex functions in a distributed setting with multiple parallel workers that are able to compute stochastic g…
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a…
Methods for Convex -Smooth Optimization: Clipping, Acceleration, and Adaptivity
Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury +4
Due to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have been gaining a lot of attention in recent years. One of the most pop…
Differentially Private Random Block Coordinate Descent
Artavazd Maranjyan, Abdurakhmon Sadiev, Peter Richtárik
Coordinate Descent (CD) methods have gained significant attention in machine learning due to their effectiveness in solving high-dimensional problems and their ability to decompose…