ArticleInformation systems frontiers : a journal of research and innovation2021
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia.
Article in Information systems frontiers : a journal of research and innovation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.Frontiers in medicine · 2024Pooled it
- Wasserstein GAN for moving differential privacy protection.Scientific reports · 2025Article
- AGASI: A Generative Adversarial Network-Based Approach to Strengthening Adversarial Image Steganography.Entropy (Basel, Switzerland) · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.Frontiers in neuroinformatics · 2025Article
- Federated learning as a smart tool for research on infectious diseases.BMC infectious diseases · 2024Review
- BOBA: Byzantine-Robust Federated Learning with Label Skewness.Proceedings of machine learning research · 2024Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection.Information systems frontiers : a journal of research and innovation · 2022Article
- Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.JMIR medical informatics · 2022Article
- GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel.Entropy (Basel, Switzerland) · 2022Review
- Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks.Frontiers in artificial intelligence · 2022Article
- A Blockchain-Based Federated Learning Method for Smart Healthcare.Computational intelligence and neuroscience · 2021Article
- Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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