  Federated Learning (FL) is a novel approach enabling several clients holding
sensitive data to collaboratively train machine learning models, without
centralizing data. The cross-silo FL setting corresponds to the case of few
($2$--$50$) reliable clients, each holding medium to large datasets, and is
typically found in applications such as healthcare, finance, or industry. While
previous works have proposed representative datasets for cross-device FL, few
realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic
research in this critical application. In this work, we propose a novel
cross-silo dataset suite focused on healthcare, FLamby (Federated Learning
AMple Benchmark of Your cross-silo strategies), to bridge the gap between
theory and practice of cross-silo FL. FLamby encompasses 7 healthcare datasets
with natural splits, covering multiple tasks, modalities, and data volumes,
each accompanied with baseline training code. As an illustration, we
additionally benchmark standard FL algorithms on all datasets. Our flexible and
modular suite allows researchers to easily download datasets, reproduce results
and re-use the different components for their research. FLamby is available
at~\url{www.github.com/owkin/flamby}.

Machine Learning;Computer Vision and Pattern Recognition