Accessibility of covariance information creates vulnerability in Federated Learning frameworks

MOTIVATION: Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algori...

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Detalles Bibliográficos
Autores principales: Huth, Manuel, Arruda, Jonas, Gusinow, Roy, Contento, Lorenzo, Tacconelli, Evelina, Hasenauer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516515/
https://www.ncbi.nlm.nih.gov/pubmed/37647639
http://dx.doi.org/10.1093/bioinformatics/btad531

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516515/
https://www.ncbi.nlm.nih.gov/pubmed/37647639
http://dx.doi.org/10.1093/bioinformatics/btad531

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