167 citations · 186 across the 4 of their papers we have counts for
7 papers
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…
On the Renyi Differential Privacy of the Shuffle Model
Antonious M. Girgis, Deepesh Data, Suhas Diggavi +2
The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model. In the shuffle model,…
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…
Successive Refinement of Privacy
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri +2
This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)? A motivating scenario is providing {\em multiple levels of privacy}…
A Converse Bound for Cache-Aided Interference Networks
Antonious M. Girgis, Ozgur Ercetin, Mohammed Nafie +1
In this paper, an interference network with arbitrary number of transmitters and receivers is studied, where each transmitter is equipped with a finite size cache. We obtain an inf…