Comparison of predictive performance between federated and local learning models.

<p>The figure illustrates the performance results for three machine learning models: (A) Logistic Regression, (B) Multi-Layer Perceptron (MLP), and (C) Random Forest. For each model, the left panel compares the mean Area Under the Curve (AUC) of the federated model (red dashed line) against th...

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Hlavní autor:	Roberta Moreira Wichmann (14259316) (author)
Další autoři:	Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
Vydáno:	2025
Témata:	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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Shrnutí:	<p>The figure illustrates the performance results for three machine learning models: (A) Logistic Regression, (B) Multi-Layer Perceptron (MLP), and (C) Random Forest. For each model, the left panel compares the mean Area Under the Curve (AUC) of the federated model (red dashed line) against the locally trained model (blue solid line), with shaded areas representing the 95% confidence intervals derived from bootstrap analysis. The right panel displays the performance gain, quantified as the Delta AUC (<i>Δ</i>AUC = AUC - AUC), where the error bars indicate the 95% CI. The horizontal dotted line represents a performance gain of zero. The x-axis for all plots is on a logarithmic scale and corresponds to the number of patients at each hospital. A consistent trend is observed across all models where federated learning generally outperforms local learning, an advantage that is particularly evident for hospitals with smaller datasets.</p>

Comparison of predictive performance between federated and local learning models.

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