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

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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Bibliographic Details
Main Authors: Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, Bert Arnrich
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
federated learning
privacy and security
privacy attack
X-ray
Online Access:https://www.mdpi.com/1424-8220/22/14/5195

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https://www.mdpi.com/1424-8220/22/14/5195

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