Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against d...

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Detalles Bibliográficos
Autores principales: Ziegler, Joceline, Pfitzner, Bjarne, Schulz, Heinrich, Saalbach, Axel, Arnrich, Bert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320045/
https://www.ncbi.nlm.nih.gov/pubmed/35890875
http://dx.doi.org/10.3390/s22145195

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320045/
https://www.ncbi.nlm.nih.gov/pubmed/35890875
http://dx.doi.org/10.3390/s22145195

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