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Found 2 papers, 1 papers with code
On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits
To address such a challenge as the online convex optimization with privacy protection, we propose a private variant of online Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data.
Differentially Private Federated Learning with Laplacian Smoothing
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users.
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