16 citations · 18 across the 2 of their papers we have counts for
3 papers
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…
Federated Survival Analysis with Discrete-Time Cox Models
Mathieu Andreux, Andre Manoel, Romuald Menuet +2
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity whi…