Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies

Abstract

Background: Federated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance. Aim: , aggregating local model predictions. Methods: and federated approaches, external geographic validation was also performed. Predictive performance in terms of discrimination [the area under the ROC curve (AUC-ROC, hereafter referred to as AUC)] and calibration (intercept and slope, and calibration graph) was measured. Results: models in 44%, 44%, and 38% of the hospitals, respectively. Conclusion: demonstrated comparable AUC and calibration. The use of FL techniques should be considered a viable option for clinical prediction model development.

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