167 citations · 219 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
Antonious M. Girgis, Deepesh Data, Suhas Diggavi
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy.…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
Antonious M. Girgis, Deepesh Data, Suhas Diggavi +2
We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) fr…