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Alexander Ziller
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End-to-end privacy preserving deep learning on multi-institutional medical imaging
G Kaissis*, A Ziller*, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, ...
Nature Machine Intelligence 3 (6), 473-484, 2021
792021
Pysyft: A library for easy federated learning
A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ...
Federated Learning Systems, 111-139, 2021
342021
Medical imaging deep learning with differential privacy
A Ziller, D Usynin, R Braren, M Makowski, D Rueckert, G Kaissis
Scientific Reports 11 (1), 1-8, 2021
262021
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
D Usynin, A Ziller, M Makowski, R Braren, D Rueckert, B Glocker, ...
Nature Machine Intelligence 3 (9), 749-758, 2021
92021
Differentially private federated deep learning for multi-site medical image segmentation
A Ziller, D Usynin, N Remerscheid, M Knolle, M Makowski, R Braren, ...
arXiv preprint arXiv:2107.02586, 2021
72021
Privacy-preserving medical image analysis
A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, IDLC Junior, ...
arXiv preprint arXiv:2012.06354, 2020
42020
Differentially private training of residual networks with scale normalisation
H Klause, A Ziller, D Rueckert, K Hammernik, G Kaissis
arXiv preprint arXiv:2203.00324, 2022
32022
Complex-valued deep learning with differential privacy
A Ziller, D Usynin, M Knolle, K Hammernik, D Rueckert, G Kaissis
arXiv preprint arXiv:2110.03478, 2021
32021
Partial sensitivity analysis in differential privacy
TT Mueller, A Ziller, D Usynin, M Knolle, F Jungmann, D Rueckert, ...
arXiv preprint arXiv:2109.10582, 2021
32021
Oktoberfest Food Dataset
A Ziller, J Hansjakob, V Rusinov, D Zügner, P Vogel, S Günnemann
arXiv preprint arXiv:1912.05007, 2019
32019
A unified interpretation of the gaussian mechanism for differential privacy through the sensitivity index
G Kaissis, M Knolle, F Jungmann, A Ziller, D Usynin, D Rueckert
arXiv preprint arXiv:2109.10528, 2021
22021
Privacy: An axiomatic approach
A Ziller, TT Mueller, R Braren, D Rueckert, G Kaissis
arXiv preprint arXiv:2203.11586, 2022
12022
An automatic differentiation system for the age of differential privacy
D Usynin, A Ziller, M Knolle, D Rueckert, G Kaissis
arXiv preprint arXiv:2109.10573, 2021
12021
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
A Ziller, D Usynin, M Knolle, K Prakash, A Trask, R Braren, M Makowski, ...
arXiv preprint arXiv:2107.04265, 2021
12021
Artificial Intelligence in Medicine and Privacy Preservation
A Ziller, J Passerat-Palmbach, A Trask, R Braren, D Rueckert, G Kaissis
Artificial Intelligence in Medicine, 1-14, 2020
12020
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
NW Remerscheid, A Ziller, D Rueckert, G Kaissis
arXiv preprint arXiv:2205.04095, 2022
2022
Distributed Machine Learning and the Semblance of Trust
D Usynin, A Ziller, D Rueckert, J Passerat-Palmbach, G Kaissis
arXiv preprint arXiv:2112.11040, 2021
2021
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
M Knolle, A Ziller, D Usynin, R Braren, MR Makowski, D Rueckert, ...
arXiv preprint arXiv:2107.04296, 2021
2021
Uncertainty Quantification of decoupled multi-component systems
A Ziller
2017
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