Evolution of the hyperparameter t in relation to the LR and MLP models.

This figure illustrates the convergence of the AUC-ROC metric across iterations of the hyperparameter t for the Logistic Regression (LR) and Multilayer Perceptron (MLP) models in the federated learning framework. The graph demonstrates how the predictive performance stabi...

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Bibliographische Detailangaben
1. Verfasser:	Roberta Moreira Wichmann (14259316) (author)
Weitere Verfasser:	Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
Veröffentlicht:	2025
Schlagworte:	Cell Biology Ecology Cancer Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified via parameter aggregation using ensemble aggregation strong inverse correlation maximum performance degradation benefits requires caution beneficial collaborative effect >&# 916 ;</ 19 mortality using 19 mortality prediction local patient volume statistically certain benefits statistical significance showed 0273 ]) due auc crossed zero study validates fl fl models demonstrated performance gain (< evaluated federated learning div >< p collaborative model ’ auc minus auc local data scarcity ci [– 0 gain ’ federated learning local validation universally certain statistical advantage 6307 ]) auc across – 0 volatile algorithm smallest hospital particularly evident network (< multicentric sample multicenter sample limited institutions layer perceptron iid ). high sensitivity findings underscore every site enabling mechanism despite achieving confidence intervals 500 ). 022 patients
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Zusammenfassung:	<p>This figure illustrates the convergence of the AUC-ROC metric across iterations of the hyperparameter <i>t</i> for the Logistic Regression (LR) and Multilayer Perceptron (MLP) models in the federated learning framework. The graph demonstrates how the predictive performance stabilizes as <i>t</i> increases, with significant convergence observed around 5 iterations. This analysis highlights the importance of hyperparameter tuning to balance model performance and computational efficiency in federated learning. This figure was developed by the authors.</p> <p>(TIFF)</p>

Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.

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