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SPEED: secure, PrivatE, and efficient deep learning., , , , and . Mach. Learn., 110 (4): 675-694 (2021)Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis., , , , and . CoRR, (2022)Distributed Learning with Curious and Adversarial Machines., , , , and . CoRR, (2023)Randomization matters How to defend against strong adversarial attacks., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7717-7727. PMLR, (2020)On the Privacy-Robustness-Utility Trilemma in Distributed Learning., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 569-626. PMLR, (2023)Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?, , , , and . PODC, page 391-401. ACM, (2021)Robust Neural Networks using Randomized Adversarial Training., , , , , , and . CoRR, (2019)On the impact of randomization on robustness in machine learning. (Impact de la randomisation sur la robustesse des modèles d'apprentissage supervisé).. PSL Research University, Paris, France, (2020)The Universal Gossip Fighter., , , , and . IPDPS, page 1162-1172. IEEE, (2022)Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 6246-6283. PMLR, (2022)