FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Longling Zhang (), Bochen Shen (), Ahmed Barnawi (), Shan Xi (), Neeraj Kumar () and Yi Wu ()
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Longling Zhang: Heilongjiang University
Bochen Shen: Heilongjiang University
Ahmed Barnawi: King Abdul Aziz University
Shan Xi: Heilongjiang University
Neeraj Kumar: Thapar Institute of Engineering and Technology
Yi Wu: Heilongjiang University

Information Systems Frontiers, 2021, vol. 23, issue 6, No 4, 1403-1415

Abstract: Abstract Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

Keywords: Generative adversarial networks; Federated learning; Differential privacy; COVID-19; Privacy protection (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-021-10144-6

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