Abstract
Federated learning (FL) ensures data privacy and security, which has been emerging as a promising approach for developing an accurate and efficient model for COVID-19 detection. This paper highlights the FL-assisted systematic review of the current state-of-the-art COVID-19 detection methods. This work conducts a comprehensive study of the relevant literature and identifies relevant articles. The included articles described various FL-based COVID-19 detection models that utilized different types of data and machine learning algorithms. Furthermore, these models have several advantages over traditional centralized models, including privacy protection, data security, and scalability. The findings of this systematic review suggest that FL-based COVID-19 detection models have great potential for accurate and efficient COVID-19 detection while protecting data privacy and security. FL requires further research to validate the findings on different metrics and enhance the performance of the models.
S. Banerjee, S. Barik, D. Das, U. Ghosh and N. C. Debnath—These authors contributed equally to this work
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Banerjee, S., Barik, S., Das, D., Ghosh, U., Debnath, N.C. (2023). Federated Learning Assisted Covid-19 Detection Model. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_35
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