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How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization.

, , and . CoRR, (2024)

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Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds., and . CoRR, (2021)FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information., , , , and . CoRR, (2021)Optimal Differentially Private Learning with Public Data., , , and . CoRR, (2023)A Stochastic Optimization Framework for Fair Risk Minimization., , , , and . Trans. Mach. Learn. Res., (2022)Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses., and . ICLR, OpenReview.net, (2023)Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?, , , , , and . CoRR, (2024)Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems., , and . SIAM J. Optim., 31 (4): 2508-2538 (2021)Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training., and . CoRR, (2021)Private Non-Convex Federated Learning Without a Trusted Server., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 5749-5786. PMLR, (2023)Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses., and . ALT, volume 201 of Proceedings of Machine Learning Research, page 986-1054. PMLR, (2023)