81 citations · 150 across the 6 of their papers we have counts for
6 papers · 1 filter
Modulated learning for private and distributed regression with just a single sample per client device
Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1
This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world applications exist to this on…
Flashback: Understanding and Mitigating Forgetting in Federated Learning
Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini +1
In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence, particularly in the presence of severe data heterogeneity among clien…
Hyperparameter Transfer Learning with Adaptive Complexity
Samuel Horváth, Aaron Klein, Peter Richtárik +1
Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models. In practice, one frequently has to solve similar hyp…
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…