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Privacy Preserving Deep Learning

The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).

Papers

Showing 4150 of 59 papers

TitleStatusHype
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
Locally Differentially Private (Contextual) Bandits LearningCode0
A generic framework for privacy preserving deep learningCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Variational Leakage: The Role of Information Complexity in Privacy LeakageCode0
Secure Data Sharing With Flow ModelCode0
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