39 citations · 88 across the 7 of their papers we have counts for
8 papers
FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li +22
External control arms can inform early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, accessing sufficient real-world or…
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…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
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…
Efficient Sparse Secure Aggregation for Federated Learning
Constance Beguier, Mathieu Andreux, Eric W. Tramel
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by…
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…
Kymatio: Scattering Transforms in Python
Mathieu Andreux, Tomás Angles, Georgios Exarchakis +15
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software packag…