Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data. (arXiv:2205.03168v1 [cs.LG])

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

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