D2P-Fed: Differentially Private Federated Learning With Efficient Communication

By Lun Wang, Ruoxi Jia, Dawn Song. 2020

29 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Peter Kairouz
2. Ken Ziyu Liu
3. Thomas Steinke
4. Vitaly Feldman
5. Borja Balle
6. Jayadev Acharya
7. Yu-Xiang Wang
8. Jonathan Ullman

Citations in the corpus

• The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation (2021, background): “Previous attempts at achieving DP using a distributed version of the discrete Gaussian mechanism have either inaccurately glossed over the fact that the sum of discrete Gaussians is not a discrete Gaussian, or assumed that all clients secretly share a seed that is used to generate the same discrete ”