167 citations · 219 across the 13 of their papers we have counts for
3 papers · 1 filter
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,…