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.
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Updated
Aug 1, 2024 - Python
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.
A Comparative Study of Gradient Clipping Techniques in Differentially Private Stochastic Gradient Descent (DP-SGD)
A differentially private spiking neural network with temporal enhanced pooling
Building an AI model for chest X-ray under patient privacy guarantees
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.
This is the Pytorch code of "Projected Federated Averaging with Heterogeneous Differential Privacy" (VLDB 2022).
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)
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