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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第一著者:	Roberta Moreira Wichmann (14259316) (author)
その他の著者:	Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
出版事項:	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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author	Roberta Moreira Wichmann (14259316)
author2	Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328)
author2_role	author author
author_facet	Roberta Moreira Wichmann (14259316) Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328)
author_role	author
dc.creator.none.fl_str_mv	Roberta Moreira Wichmann (14259316) Murilo Afonso Robiati Bigoto (22676715) Alexandre Dias Porto Chiavegatto Filho (14259328)
dc.date.none.fl_str_mv	2025-11-24T18:35:34Z
dc.identifier.none.fl_str_mv	10.1371/journal.pcbi.1013695.s001
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/Evolution_of_the_hyperparameter_i_t_i_in_relation_to_the_LR_and_MLP_models_/30697788
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	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
dc.title.none.fl_str_mv	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<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>
eu_rights_str_mv	openAccess
id	Manara_3e8e8c0bcbf10c38ba18478cdaec376f
identifier_str_mv	10.1371/journal.pcbi.1013695.s001
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/30697788
publishDate	2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.Roberta Moreira Wichmann (14259316)Murilo Afonso Robiati Bigoto (22676715)Alexandre Dias Porto Chiavegatto Filho (14259328)Cell BiologyEcologyCancerScience PolicyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvia parameter aggregationusing ensemble aggregationstrong inverse correlationmaximum performance degradationbenefits requires cautionbeneficial collaborative effect>&# 916 ;</19 mortality using19 mortality predictionlocal patient volumestatistically certain benefitsstatistical significance showed0273 ]) dueauc crossed zerostudy validates flfl models demonstratedperformance gain (<evaluated federated learningdiv >< pcollaborative model ’auc minus auclocal data scarcityci [– 0gain ’federated learninglocal validationuniversally certainstatistical advantage6307 ])auc across– 0volatile algorithmsmallest hospitalparticularly evidentnetwork (<multicentric samplemulticenter samplelimited institutionslayer perceptroniid ).high sensitivityfindings underscoreevery siteenabling mechanismdespite achievingconfidence intervals500 ).022 patients<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>2025-11-24T18:35:34ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013695.s001https://figshare.com/articles/figure/Evolution_of_the_hyperparameter_i_t_i_in_relation_to_the_LR_and_MLP_models_/30697788CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306977882025-11-24T18:35:34Z
spellingShingle	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models. Roberta Moreira Wichmann (14259316) 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
status_str	publishedVersion
title	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
title_full	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
title_fullStr	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
title_full_unstemmed	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
title_short	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
title_sort	Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
topic	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

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

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