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Personalized federated learning for the detection of COVID-19

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Abstract

Federated learning (FL) plays a crucial role in COVID-19 detection by enabling collaborative analysis of medical data while preserving privacy. In this context, medical data is typically dispersed across various medical institutions. FL allows these institutions to collectively train a deep learning model without the need to centralize or transfer the data. But Federated Averaging (FedAvg), the default setting of FL, does not consider personalizing the deep learning model to each institution and cannot ensure model performance when data heterogeneity heavily exists among institutions. In this paper, we investigate Personalized Federated Averaging (PerFedAvg), a personalized variant of the FL framework that aims to train a model that can be tailored to each institution after a few iterations of local gradient descent on the data of that institution while safeguarding the privacy of that institution’s patients. We used data from a publicly available Kaggle dataset that contained chest X-ray images of COVID-19, pneumonia, and normal patients, and created two federated settings that would reflect the heterogeneity of data in terms of distribution, and quantity across medical institutions in the real world. We experimentally demonstrate that PerFedAvg outperforms the standard FedAvg technique in such settings with an increase in accuracy by 3.11%-9.30%. With the help of this research, nations and institutions might quickly construct effective artificial intelligence during pandemics, easing the load of centralized aggregation of copious volumes of sensitive data.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the Kaggle repository https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia.

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Acknowledgements

The authors, thanks to all the anonymous reviewers of Multimedia Tools and Applications Journal for their constructive remarks and fruitful suggestions to improve the manuscript.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Information Technology, Nagpur, India

    Dharwada Sesha Sriram, Aseem Ranjan, Vedant Ghuge, Naveen Rathore, Raghav Agarwal, Tausif Diwan & Jitendra V. Tembhurne

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  1. Dharwada Sesha Sriram
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  2. Aseem Ranjan
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Correspondence to Tausif Diwan.

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Sriram, D.S., Ranjan, A., Ghuge, V. et al. Personalized federated learning for the detection of COVID-19. Multimed Tools Appl 83, 29067–29084 (2024). https://doi.org/10.1007/s11042-023-16810-y

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