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

Author

Listed:
  • 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
    University of Petroleum and Energy Studies
    Asia University)

  • Yi Wu

    (Heilongjiang University)

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.

Suggested Citation

  • Longling Zhang & Bochen Shen & Ahmed Barnawi & Shan Xi & Neeraj Kumar & Yi Wu, 2021. "FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia," Information Systems Frontiers, Springer, vol. 23(6), pages 1403-1415, December.
  • Handle: RePEc:spr:infosf:v:23:y:2021:i:6:d:10.1007_s10796-021-10144-6
    DOI: 10.1007/s10796-021-10144-6
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    References listed on IDEAS

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    1. Wenhua Liang & Jianhua Yao & Ailan Chen & Qingquan Lv & Mark Zanin & Jun Liu & SookSan Wong & Yimin Li & Jiatao Lu & Hengrui Liang & Guoqiang Chen & Haiyan Guo & Jun Guo & Rong Zhou & Limin Ou & Niyun, 2020. "Early triage of critically ill COVID-19 patients using deep learning," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
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    4. Fereshteh Ghahramani & Jingguo Wang, 0. "Impact of Smartphones on Quality of Life: A Health Information Behavior Perspective," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    5. Davy Preuveneers & Giuseppe Garofalo & Wouter Joosen, 2021. "Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom," Information Systems Frontiers, Springer, vol. 23(1), pages 151-164, February.
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    Cited by:

    1. Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.
    2. Gaith Rjoub & Omar Abdel Wahab & Jamal Bentahar & Robin Cohen & Ahmed Saleh Bataineh, 2024. "Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection," Information Systems Frontiers, Springer, vol. 26(4), pages 1261-1278, August.

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