Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. - César Sabater; Aurélien Bellet; Jan Ramon (CoRR, 2020)

An accurate, scalable and verifiable protocol for federated differentially private averaging. - César Sabater; Aurélien Bellet; Jan Ramon (Mach. Learn., 2022)

Efficient and Robust Protocols for Privacy-Preserving Semi-Decentralized Machine Learning. (Protocoles Efficaces et Robustes pour l'Apprentissage Automatique Semi-Décentralisé Préservant la Confidentialité). - César Sabater (2022)

Private Sampling with Identifiable Cheaters. - César Sabater; Florian Hahn 001; Andreas Peter 001; Jan Ramon (Proc. Priv. Enhancing Technol., 2023)

Differentially private and decentralized randomized power method. - Julien Nicolas; César Sabater; Mohamed Maouche; Sonia Ben Mokhtar; Mark Coates (CoRR, 2024)

Scrutinizing the Vulnerability of Decentralized Learning to Membership Inference Attacks. - Ousmane Touat; Jezekael Brunon; Yacine Belal; Julien Nicolas; Mohamed Maouche; César Sabater; Sonia Ben Mokhtar (CoRR, 2024)

Secure Federated Graph-Filtering for Recommender Systems. - Julien Nicolas; César Sabater; Mohamed Maouche; Sonia Ben Mokhtar; Mark Coates (CoRR, 2025)

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation. - César Sabater; Sonia Ben Mokhtar; Jan Ramon (CoRR, 2025)