Privacy Amplification by Decentralization. - Edwige Cyffers; Aurélien Bellet (AISTATS, 2022)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. - Edwige Cyffers; Mathieu Even; Aurélien Bellet; Laurent Massoulié (NeurIPS, 2022)

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Jean Ogier du Terrail; Samy-Safwan Ayed; Edwige Cyffers; Felix Grimberg; Chaoyang He 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. - Edwige Cyffers; Aurélien Bellet; Debabrota Basu (ICML, 2023)

Differentially Private Decentralized Learning with Random Walks. - Edwige Cyffers; Aurélien Bellet; Jalaj Upadhyay (ICML, 2024)

Privacy Attacks in Decentralized Learning. - Abdellah El Mrini; Edwige Cyffers; Aurélien Bellet (ICML, 2024)

Optimal Classification under Performative Distribution Shift. - Edwige Cyffers; Muni Sreenivas Pydi; Jamal Atif; Olivier Cappé (NeurIPS, 2024)

Differential Privacy for Decentralized Learning. (Confidentialité différentielle pour l'apprentissage décentralisé). - Edwige Cyffers (2024)

Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning. - Marc Damie; Edwige Cyffers (CoRR, 2025)