Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

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The framework of approximate differential privacy is considered, and augmented by leveraging the notion of "the total variation of a (privacy-preserving) mechanism" (denoted by eta-TV). With this refinement, an exact composition result is derived, and show ...

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

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Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Learning Hand State Estimation for a Light Exoskeleton

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Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

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Total Variation Meets Differential Privacy