Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties
By César Sabater, Aurélien Bellet, J. Ramon. 2020
26 citations recorded by the source corpus.
Subfields: Federated & Distributed Learning, Privacy & Security
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Federated & Distributed Learning
- Edwige Cyffers
- Aurélien Bellet
- María Victoria Luzón
- Nuria Rodr'iguez-Barroso
- Francisco Herrera
- Daniel Jim'enez L'opez
- Eugenio Martínez-Cámara
- Ahmed M Abdelhadi
In Privacy & Security
- Edwige Cyffers
- Aurélien Bellet
- María Victoria Luzón
- Nuria Rodr'iguez-Barroso
- Francisco Herrera
- Daniel Jim'enez L'opez
- Eugenio Martínez-Cámara
- Ahmed M Abdelhadi
Citations in the corpus
- Privacy Amplification by Decentralization (2020, background): “…user contributions (Dwork et al., 2006a; Shi et al., 2011; Bonawitz et al., 2017; Chan et al., 2012b; Jayaraman et al., 2018; Bell et al., 2020; Sabater et al., 2020) or to securely shuffle the set of user messages so as to hide their source (Cheu et al., 2019; Erlingsson et al., 2019; Balle et…”
- Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges (2022, background): “aggregation schemes have been improved in terms of privacy with the addition of DP mechanisms [149, 150, 151, 152] .”