39 citations · 50 across the 5 of their papers we have counts for
5 papers
DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning
Julien T. T. Vignoud, Valérian Rousset, Hugo El Guedj +28
Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the…
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…
Optimal Model Averaging: Towards Personalized Collaborative Learning
Felix Grimberg, Mary-Anne Hartley, Sai P. Karimireddy +1
In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node. One s…
WAFFLE: Weighted Averaging for Personalized Federated Learning
Martin Beaussart, Felix Grimberg, Mary-Anne Hartley +1
In federated learning, model personalization can be a very effective strategy to deal with heterogeneous training data across clients. We introduce WAFFLE (Weighted Averaging For F…
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo +13
Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the d…