AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025080013
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ORIGINAL RESEARCH ARTICLE

A hierarchical federated learning-based health stack for future pandemic preparedness

Rojalini Tripathy1 ,  Asmit Balabantaray2 ,  Nisarg Shah2 ,  Prashant Kumar Jha3 ,  Ajay Kumar Gogineni1 ,  Atri Mukhopadhyay1 ,  Kisor Kumar Sahu3,4* ,  Padmalochan Bera1*
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1 School of Electrical and Computer Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India
2 School of Mechanical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India
3 School of Minerals, Metallurgical and Materials Engineering, Indian Institute of Technology Bhubaneswar, Odisha, India
4 Virtual and Augmented Reality Center of Excellence, Indian Institute of Technology Bhubaneswar, Odisha, India
AIH, 025080013 https://doi.org/10.36922/AIH025080013
Received: 21 February 2025 | Revised: 8 June 2025 | Accepted: 9 June 2025 | Published online: 30 June 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The COVID-19 pandemic, one of the most disruptive global health crises in recent history, exposed critical vulnerabilities in existing healthcare infrastructure. Given the likelihood of future pandemics, it is essential to build a resilient, collaborative, synergistic, data-driven, and intelligent digital healthcare software. It should be meticulously designed and selectively curated to enhance early detection, rapid response, and efficient containment of outbreaks. In this article, we propose a federated learning (FL)-based health stack that prioritizes privacy while fostering collaborative intelligence among hospitals or client nodes. Our framework incorporates hierarchical FL, Byzantine-resilient information-theoretic FL (ByITFL), homomorphic encryption, and blockchain-based smart contracts to ensure secure collaboration among healthcare institutions without sharing raw data. Hierarchical FL leverages multilevel model aggregation to enhance model convergence, scalability, and resilience. ByITFL strengthens security by incorporating trust mechanisms and information-theoretic privacy scoring, while blockchain-based smart contracts ensure transparent, verifiable coordination among participating nodes. Furthermore, deep vulnerability detection using optimized averaged stochastic gradient descent weight-dropped long short-term memory models may further enhance the framework’s security, enabling threat identification during decentralized data exchanges. Experimental results show that the proposed hierarchical FL model achieves 94.23% accuracy on the modified National Institute of Standards and Technology dataset, outperforming federated averaging (92.66%) under the same environments. In addition, communication analysis proved that the overall transmission is minimized by collecting updates at local servers before sending them to central servers. Therefore, it is nearly a future-ready technology that can be implemented without many geopolitical issues, even in the case of hypersensitive global situations.

Keywords
Global pandemics
Health stack
Federated learning
Medical data privacy
Machine learning
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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