Search Results for author: Mathieu Andreux

Found 5 papers, 2 papers with code

Differentially Private Federated Learning for Cancer Prediction

1 code implementation8 Jan 2021 Constance Beguier, Jean Ogier du Terrail, Iqraa Meah, Mathieu Andreux, Eric W. Tramel

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 private computing for genomic data.

Federated Learning

Siloed Federated Learning for Multi-Centric Histopathology Datasets

no code implementations17 Aug 2020 Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, Eric W. Tramel

While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common.

Domain Adaptation Federated Learning +1

Efficient Sparse Secure Aggregation for Federated Learning

no code implementations29 Jul 2020 Constance Beguier, Mathieu Andreux, Eric W. Tramel

Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets.

Federated Learning

Federated Survival Analysis with Discrete-Time Cox Models

no code implementations16 Jun 2020 Mathieu Andreux, Andre Manoel, Romuald Menuet, Charlie Saillard, Chloé Simpson

Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy.

Federated Learning Survival Analysis

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