  Large-scale collections of electronic records constitute both an opportunity
for the development of more accurate prediction models and a threat for
privacy. To limit privacy exposure new privacy-enhancing techniques are
emerging such as federated learning which enables large-scale data analysis
while avoiding the centralization of records in a unique database that would
represent a critical point of failure. Although promising regarding privacy
protection, federated learning prevents using some data-cleaning algorithms
thus inducing new biases. In this work we focus on the recurrent problem of
duplicated records that, if not handled properly, may cause over-optimistic
estimations of a model's performances. We introduce and discuss stratified
cross-validation, a validation methodology that leverages stratification
techniques to prevent data leakage in federated learning settings without
relying on demanding deduplication algorithms.

Machine Learning;Machine Learning;Methodology