4 citations · 6 across the 4 of their papers we have counts for
4 papers
Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective
Masoud Kavian, Romain Chor, Milad Sefidgaran +1
In this paper, we investigate the effect of data heterogeneity across clients on the performance of distributed learning systems, i.e., one-round Federated Learning, as measured by…
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi +1
We investigate the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, we study the evolution of the generalization error with t…
More Communication Does Not Result in Smaller Generalization Error in Federated Learning
Romain Chor, Milad Sefidgaran, Abdellatif Zaidi
We study the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, there are devices or clients, each holding an independent o…
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi
In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms. Specifically, ther…