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

arXiv: https://arxiv.org/abs/2001.08090 · HTML: https://mlsanity.com/paper/2001.08090/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques](https://mlsanity.com/paper/2203.01394/) (2022)
- [Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)](https://mlsanity.com/paper/2301.10888/) (2023)

## Abstract (arXiv)

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.