1. Stratified cross-validation for unbiased and privacy-preserving federated learning
- Author
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Bey, R., Goussault, R., Benchoufi, M., and Porcher, R.
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning ,Statistics - Methodology - 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., Comment: 13 pages, 5 figures
- Published
- 2020
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