Federated Learning based Covid-19 Detection

Deepraj Chowdhury, Soham Banerjee, Madhushree Sannigrahi, Arka Chakraborty, Anik Das, Ajoy Dey, Ashutosh Dhar Dwivedi*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

53 Downloads (Pure)


The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.
TidsskriftExpert Systems
Udgave nummer5
Antal sider12
StatusUdgivet - jun. 2023

Bibliografisk note

Published online: 2 November 2022.


  • COVID-19
  • CXR images
  • Cybersecurity
  • Federated learning
  • Internet of medial things (IoMT)
  • Privacy
  • Transfer learning
  • Xception