12 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 12 cited
Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah +2
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm w…
cs.LG2021★ 2 cited
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason +19
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML sy…
cs.LG2019★ 9 cited
Element Level Differential Privacy: The Right Granularity of Privacy
Hilal Asi, John Duchi, Omid Javidbakht
Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning c…