16 citations · 19 across the 2 of their papers we have counts for
3 papers
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Tanguy Marchand, Boris Muzellec, Constance Beguier +2
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning. In this paper, we i…
Differentially Private Federated Learning for Cancer Prediction
Constance Beguier, Jean Ogier du Terrail, Iqraa Meah +2
Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of pri…
Siloed Federated Learning for Multi-Centric Histopathology Datasets
Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier +1
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially…