3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CR2025
Hush! Protecting Secrets During Model Training: An Indistinguishability Approach
Arun Ganesh, Brendan McMahan, Milad Nasr +2
We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contai…
cs.LG2022★ 3 cited
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan +2
Federated learning (FL) is a framework for machine learning across heterogeneous client devices in a privacy-preserving fashion. To date, most FL algorithms learn a "global" server…