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

arXiv: https://arxiv.org/abs/2302.08688 · HTML: https://mlsanity.com/paper/2302.08688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing](https://mlsanity.com/paper/2007.09236/) (2020)
- [Dynamic Fusion based Federated Learning for COVID-19 Detection](https://mlsanity.com/paper/2009.10401/) (2020)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximation Algorithms for String Kernel Based Sequence Classification](https://mlsanity.com/paper/1712.04264/) (2017)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [Effective and scalable clustering of SARS-CoV-2 sequences](https://mlsanity.com/paper/2108.08143/) (2021)
- [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://mlsanity.com/paper/2110.09622/) (2021)
- [Combinatorial Trace Method for Network Immunization](https://mlsanity.com/paper/1912.12070/) (2019)
- [Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach](https://mlsanity.com/paper/2102.12245/) (2021)
- [Spectral Methods for Immunization of Large Networks](https://mlsanity.com/paper/1711.00791/) (2017)
- [Scalable Approximation Algorithm for Network Immunization](https://mlsanity.com/paper/1711.00784/) (2017)
- [Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection](https://mlsanity.com/paper/2103.11269/) (2021)

## Built on by (most cited first, 4 of 4)

- [ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation](https://mlsanity.com/paper/2304.02891/) (2023)
- [Virus2Vec: Viral Sequence Classification Using Machine Learning](https://mlsanity.com/paper/2304.12328/) (2023)
- [Federated learning, ethics, and the double black box problem in medical AI](https://mlsanity.com/paper/2504.20656/) (2025)
- [EPIC: Enhancing Privacy through Iterative Collaboration](https://mlsanity.com/paper/2411.05167/) (2024)

## 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.