Stratified cross-validation for unbiased and privacy-preserving federated learning

Understand

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.