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Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning

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Information Management and Big Data (SIMBig 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2142))

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Abstract

This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike sequences in a distributed way, without data sharing, to detect different variants of this rapidly mutating coronavirus. Our method maintains the confidentiality of local data (that could be stored in different locations) yet allows us to reliably detect and identify different known and unknown variants of the novel coronavirus SARS-CoV-2. Using the proposed approach, we achieve an overall accuracy of \(93\%\) on the coronavirus variant identification task. We also provide details regarding how the proposed model follows the main laws of federated learning, such as Laws of data ownership, data privacy, model aggregation, and model heterogeneity. Since the proposed model is distributed, it could scale on “Big Data” easily. We plan to use this proof-of-concept to implement a privacy-preserving pandemic response strategy.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Georgia State University, Atlanta, GA, 30303, USA

    Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali & Murray Patterson

  2. Lahore University of Management Sciences, Lahore Punjab, 54792, Pakistan

    Imdad Ullah Khan

Authors
  1. Prakash Chourasia
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  2. Taslim Murad
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  3. Zahra Tayebi
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  4. Sarwan Ali
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  5. Imdad Ullah Khan
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  6. Murray Patterson
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Corresponding author

Correspondence to Sarwan Ali .

Editor information

Editors and Affiliations

  1. National Institutes of Health (NIH), Bethesda, MD, USA

    Juan Antonio Lossio-Ventura

  2. Eastern New Mexico University, Portales, NM, USA

    Eduardo Ceh-Varela

  3. CNRS, LIRIS, Lyon, France

    Genoveva Vargas-Solar

  4. University of São Paulo, São Paulo, Brazil

    Ricardo Marcacini

  5. Mines ParisTech, Paris, France

    Claude Tadonki

  6. Instituto Politecnico Nacional, Mexico City, Mexico

    Hiram Calvo

  7. Léonard de Vinci Pôle Universitaire, Paris, France

    Hugo Alatrista-Salas

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Chourasia, P., Murad, T., Tayebi, Z., Ali, S., Khan, I.U., Patterson, M. (2024). Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2023. Communications in Computer and Information Science, vol 2142. Springer, Cham. https://doi.org/10.1007/978-3-031-63616-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-63616-5_6

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