2 citations · 3 across the 4 of their papers we have counts for
4 papers
Learning from straggler clients in federated learning
Andrew Hard, Antonious M. Girgis, Ehsan Amid +4
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from…
Multi-Message Shuffled Privacy in Federated Learning
Antonious M. Girgis, Suhas Diggavi
We study differentially private distributed optimization under communication constraints. A server using SGD for optimization aggregates the client-side local gradients for model u…
Differentially Private Stochastic Linear Bandits: (Almost) for Free
Osama A. Hanna, Antonious M. Girgis, Christina Fragouli +1
In this paper, we propose differentially private algorithms for the problem of stochastic linear bandits in the central, local and shuffled models. In the central model, we achieve…
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
Kaan Ozkara, Antonious M. Girgis, Deepesh Data +1
A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personali…