Web: http://arxiv.org/abs/2205.03168

May 9, 2022, 1:11 a.m. | Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, Bert Arnrich

cs.LG updates on arXiv.org arxiv.org

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 privacy
attacks on DenseNet121 and ResNet50 network architectures. We simulated a
federated environment by distributing images from the public CheXpert and
Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private
baseline models achieved an area under the …

arxiv attacks classification data federated learning learning x-ray

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