The extension library of Pytorch Opacus which enables fast and efficient DP-SGD training via example-wise gradient computation

A differentially private spiking neural network with temporal enhanced pooling

A Comparative Study of Gradient Clipping Techniques in Differentially Private Stochastic Gradient Descent (DP-SGD)

Securing Collaborative Medical AI by Using Differential Privacy

In this project we add differential privacy into an openset recognizer.to implement DP we use opacus library.

Code for the paper "PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning" by L. Corbucci, M. A. Heikkilä, D.S. Noguero, A. Monreale, N. Kourtellis.

Implementation of Opacus DP in flower.

Imagenet-scale DP-SGD training using Pytorch Opacus

Building an AI model for chest X-ray under patient privacy guarantees

Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)