81 citations · 181 across the 12 of their papers we have counts for
3 papers · 1 filter
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth +1
In this work, we consider the optimization formulation of personalized federated learning recently introduced by Hanzely and Richtárik (2020) which was shown to give an alternative…
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Samuel Horváth, Peter Richtárik
Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed compute systems. A key bottleneck of such systems is th…
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Samuel Horváth, Lihua Lei, Peter Richtárik +1
Adaptivity is an important yet under-studied property in modern optimization theory. The gap between the state-of-the-art theory and the current practice is striking in that algori…