Schematic visualization of the federated learning approaches.

<p>(A) Parameter averaging framework (Scenario I): In this system, participants (hospitals) compute model coefficients and send them anonymously to the server for aggregation. The server then returns the updated global model to the participants in an iterative process. (B) Decision tree aggreg...

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主要作者:	Roberta Moreira Wichmann (14259316) (author)
其他作者:	Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
出版:	2025
主題:	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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Schematic visualization of the federated learning approaches.

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