FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information
Open accessAbstract
Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.
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A tradeoff between model accuracy and desired privacy level needs to be considered when using differential privacy.
“Hence, a tradeoff between model accuracy and desired privacy level needs to be considered [26].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [26]
In federated learning for COVID-19 detection using chest X-rays and symptoms across multiple hospitals, non-IID privacy preservation is achieved by adding differential privacy stochastic gradient descent, which provides resilience to adaptive attacks with auxiliary information.
“In addition to the measures to improve model accuracy for data, non-IID privacy preservation is achieved by adding the differential privacy stochastic gradient descent (DP-SGD) which provides required resilience to adaptive attacks auxiliary information [26].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [26]
By increasing the number of participants in the federated learning system, a high differential privacy guarantee can be achieved with minimal effect on model accuracy.
“The results showed that by increasing the number of participants in the FL system high differential guarantee can be achieved with minimal effect on model accuracy [26].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [26]
DP poses all relevant characteristics to mitigate all risks associated with information leakage during model parameter exchange.
“DP also poses all relevant characteristics to mitigate all risks associated with information leakage during model parameter exchange [26,30,32].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [26]
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Federated learning as a smart tool for research on infectious diseases
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Role of federated learning in healthcare systems: A survey
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FedMDO: Privacy-Preserving Federated Learning via Mixup Differential Objective
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Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
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Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
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Artificial Intelligence in Epidemic Modeling and Pandemic Preparedness: A Comprehensive Review
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- Authors
- Ho, T.-T., Tran, K.-D., & Huang, Y.
- Affiliations
- Research Center for Information Technology Innovation, Academia Sinica (TW)
- Corresponding author
- Ho, Trang-Thi
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-05-13
- Volume
- 22
- Issue
- 10
- Pages
- 3728
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9147951
- OpenAlex
- W4280580930
- Cited by
- 44
- References
- 83
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 13 · 2023: 16 · 2022: 3
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Tamkang University Institutional Repository (TKUIR) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology of the Republic of China (MOST109-2221-E-001-019-MY3, AS-KPQ-109-DSTCP)
- Academia Sinica (MOST109-2221-E-001-019-MY3, AS-KPQ-109-DSTCP)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 detection, federated learning, differential privacy, DP-SGD, chest X-ray images, non-IID data, spatial pyramid pooling, 2D convolutional neural network, privacy-preserving machine learning, decentralized learning
- MeSH terms
- COVID-19, Humans, Thorax, X-Rays, Neural Networks, Computer, Privacy
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