Towards Decentralized Deep Learning with Differential Privacy

By Hsin-Pai Cheng, P. Yu, Haojing Hu, Syed Zawad, Feng Yan, Shiyu Li, Hai Helen Li, Yiran Chen. 2019

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

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

In Artificial Intelligence

1. Edwige Cyffers
2. Aurélien Bellet
3. Ahmed M Abdelhadi
4. Ahmed El Ouadrhiri

In Computer Networks and Communications

1. Aurélien Bellet
2. Edwige Cyffers

Citations in the corpus

• Privacy Amplification by Decentralization (2020, methodology): “In LDP, random perturbations are performed locally by each user, making it convenient to design private versions of fully de-centralized algorithms in this model (see e.g., Huang et al., 2015; Bellet et al., 2018; Li et al., 2018; Cheng et al., 2019; Zhang et al., 2018; Xu et al., 2020).”