Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Background: Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an app...

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Veröffentlicht in:	JMIR medical informatics Jg. 9; H. 6; S. e26598
Hauptverfasser:	Cha, Dongchul, Sung, MinDong, Park, Yu-Rang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Toronto JMIR Publications 01.06.2021
Schlagworte:	Classification Collaboration Colorectal cancer Datasets Laboratories Machine learning Neural networks Original Paper Patients Privacy Servers Variables
ISSN:	2291-9694, 2291-9694
Online-Zugang:	Volltext
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