| Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings J Ogier du Terrail, SS Ayed, E Cyffers, F Grimberg, C He, R Loeb, ... Advances in Neural Information Processing Systems 35, 5315-5334, 2022 | 136 | 2022 |
| Privacy amplification by decentralization E Cyffers, A Bellet International Conference on Artificial Intelligence and Statistics, 5334-5353, 2022 | 59 | 2022 |
| Muffliato: Peer-to-peer privacy amplification for decentralized optimization and averaging E Cyffers, M Even, A Bellet, L Massoulié Advances in Neural Information Processing Systems 35, 15889-15902, 2022 | 28 | 2022 |
| From noisy fixed-point iterations to private ADMM for centralized and federated learning E Cyffers, A Bellet, D Basu International Conference on Machine Learning, 6683-6711, 2023 | 9 | 2023 |
| Optimal classification under performative distribution shift E Cyffers, MS Pydi, J Atif, O Cappé arXiv preprint arXiv:2411.02023, 2024 | 2 | 2024 |
| Privacy Attacks in Decentralized Learning AE Mrini, E Cyffers, A Bellet arXiv preprint arXiv:2402.10001, 2024 | 2 | 2024 |
| Differentially Private Decentralized Learning with Random Walks E Cyffers, A Bellet, J Upadhyay arXiv preprint arXiv:2402.07471, 2024 | 2 | 2024 |
| Confidentialité différentielle pour l’apprentissage décentralisé E Cyffers | | 2024 |
| Differential Privacy for Decentralized Learning E Cyffers Université de Lille 1, Sciences et Technologies; CRIStAL UMR 9189, 2024 | | 2024 |
| La confidentialité différentielle: quelle quantification de la privacy dans le monde de l’apprentissage automatique? E Cyffers < bound method Organization. get_name_with_acronym of< Organization: DUMAS …, 2021 | | 2021 |
