A Comparative Study of Gradient Clipping Techniques in Differentially Private Stochastic Gradient Descent (DP-SGD)
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Updated
Jun 13, 2024 - Python
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.
Securing Collaborative Medical AI by Using Differential Privacy
Building an AI model for chest X-ray under patient privacy guarantees
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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