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Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants.

, , , , and . SRDS, page 94-120. IEEE, (2022)

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Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?, , , , and . PODC, page 391-401. ACM, (2021)The Universal Gossip Fighter., , , , and . IPDPS, page 1162-1172. IEEE, (2022)Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants., , , , and . SRDS, page 94-120. IEEE, (2022)On the Inherent Anonymity of Gossiping., , , , and . DISC, volume 281 of LIPIcs, page 24:1-24:19. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2023)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)On the robustness of randomized classifiers to adversarial examples., , , , , and . Mach. Learn., 111 (9): 3425-3457 (2022)On the Impossible Safety of Large AI Models., , , , , , and . CoRR, (2022)Overcoming the Challenges of Batch Normalization in Federated Learning., , , , and . CoRR, (2024)