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

Securing Collaborative Medical AI by Using Differential Privacy

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

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

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)

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.

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

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

Implementation of Opacus DP in flower.

Imagenet-scale DP-SGD training using Pytorch Opacus