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• W3000795845 abstract "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." @default.
• W3000795845 created "2020-01-30" @default.
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• W3000795845 date "2020-07-04" @default.
• W3000795845 modified "2023-10-12" @default.
• W3000795845 title "Fold-stratified cross-validation for unbiased and privacy-preserving federated learning" @default.
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• W3000795845 doi "https://doi.org/10.1093/jamia/ocaa096" @default.
• W3000795845 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7647321" @default.
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