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

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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