Abstract
The COVID-19 pandemic has created significant global health and socioeconomic challenges, which creates the need for efficient and effective early detection methods. Several traditional machine learning (ML) and deep learning (DL) approaches have been used in the detection of COVID-19. However, ML and DL strategies face challenges like transmission delays, a lack of computing power, communication delays, and privacy concerns. Federated Learning (FL), has emerged as a promising method for training models on decentralized data while ensuring privacy. In this paper, we present a novel FL framework for early detection of COVID-19 using the Particle Swarm Optimization (PSO) model. The proposed framework uses advantages of both FL and PSO. By employing the PSO technique the model aims to achieve faster convergence and improved performance. In order to validate the effectiveness of the proposed approach, we performed experiments using a COVID-19 image dataset which was collected from different healthcare institutions. The results indicate that our approach is more effective as it achieves a higher accuracy rate of 94.36% which is higher when compared to traditional centralized learning approaches. Furthermore, the FL framework ensures data privacy and security by keeping sensitive patient information decentralized and only sharing aggregated model updates during the training process.
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This work is supported by Zhejiang Key Research and Development Project under Grant 2017C01043.
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Dasaradharami Reddy, K. et al. (2024). Federated Learning Using the Particle Swarm Optimization Model for the Early Detection of COVID-19. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_32
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DOI: https://doi.org/10.1007/978-981-99-8132-8_32
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