mlsanityData to 7 Oct 2026

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

arXiv 2302.08688 ↗First posted 17 Feb 2023cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 58 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

19 of 58 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2021Model-Contrastive Federated Learningcited by 762
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  5. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  6. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  7. 2021Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequencescited by 30
  8. 2021A k-mer Based Approach for SARS-CoV-2 Variant Identificationcited by 23
  9. 2022PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequencescited by 22
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  11. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  12. 2022Efficient Approximate Kernel Based Spike Sequence Classificationcited by 18
7 more
  1. 2021Effective and scalable clustering of SARS-CoV-2 sequencescited by 15
  2. 2021Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variantscited by 14
  3. 2019Combinatorial Trace Method for Network Immunizationcited by 12
  4. 2021Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approachcited by 12
  5. 2017Spectral Methods for Immunization of Large Networkscited by 9
  6. 2017Scalable Approximation Algorithm for Network Immunizationcited by 8
  7. 2021Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infectioncited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023ViralVectors: Compact and Scalable Alignment-free Virome Feature Generationcited by 1
  2. 2023Virus2Vec: Viral Sequence Classification Using Machine Learningcited by 1
  3. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  4. 2024EPIC: Enhancing Privacy through Iterative Collaborationcited by 0