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Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise., , , , , и . CoRR, (2020)Besting the Black-Box: Barrier Zones for Adversarial Example Defense., , , , и . IEEE Access, (2022)Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD., , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 6392-6400. PMLR, (2019)Splitting the Interpose PUF: A Novel Modeling Attack Strategy., , , , , , , и . IACR Cryptology ePrint Archive, (2019)Considerations on the Theory of Training Models with Differential Privacy., и . CoRR, (2023)A Retrospective on Path ORAM., , , , , , и . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (8): 1572-1576 (2020)Path ORAM: An Extremely Simple Oblivious RAM Protocol., , , , , , , и . J. ACM, 65 (4): 18:1-18:26 (2018)Differential Private Hogwild! over Distributed Local Data Sets., , , , и . CoRR, (2021)Quantifying and Mitigating Privacy Risks for Tabular Generative Models., , , , , и . CoRR, (2024)Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution., , и . CoRR, (2022)