DPCOVID: Privacy-Preserving Federated Covid-19 Detection
- URL: http://arxiv.org/abs/2110.13760v1
- Date: Tue, 26 Oct 2021 15:09:00 GMT
- Title: DPCOVID: Privacy-Preserving Federated Covid-19 Detection
- Authors: Trang-Thi Ho, Yennun-Huang
- Abstract summary: Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health.
We present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images.
- Score: 2.1930130356902207
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Coronavirus (COVID-19) has shown an unprecedented global crisis by the
detrimental effect on the global economy and health. The number of COVID-19
cases has been rapidly increasing, and there is no sign of stopping. It leads
to a severe shortage of test kits and accurate detection models. A recent study
demonstrated that the chest X-ray radiography outperformed laboratory testing
in COVID-19 detection. Therefore, using chest X-ray radiography analysis can
help to screen suspected COVID-19 cases at an early stage. Moreover, the
patient data is sensitive, and it must be protected to avoid revealing through
model updates and reconstruction from the malicious attacker. In this paper, we
present a privacy-preserving Federated Learning system for COVID-19 detection
based on chest X-ray images. First, a Federated Learning system is constructed
from chest X-ray images. The main idea is to build a decentralized model across
multiple hospitals without sharing data among hospitals. Second, we first show
that the accuracy of Federated Learning for COVID-19 identification reduces
significantly for Non-IID data. We then propose a strategy to improve model's
accuracy on Non-IID COVID-19 data by increasing the total number of clients,
parallelism (client fraction), and computation per client. Finally, we apply a
Differential Privacy Stochastic Gradient Descent (DP-SGD) to enhance the
preserving of patient data privacy for our Federated Learning model. A strategy
is also proposed to keep the robustness of Federated Learning to ensure the
security and accuracy of the model.
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