DPCOVID: Privacy-Preserving Federated Covid-19 Detection

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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