IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3428-d1511914.html
   My bibliography  Save this article

Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems

Author

Listed:
  • Igor Kabashkin

    (Engineering Faculty, Transport and Telecommunication Institute, Lauvas 2, LV-1019 Riga, Latvia)

Abstract

The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the broad knowledge capture of foundation models with the privacy-preserving and adaptive nature of federated learning. Through extensive simulations on a representative aircraft fleet, the integrated FM + FL approach demonstrated consistently superior performance compared to standalone implementations across multiple key metrics, including prediction accuracy, model size efficiency, and convergence speed. The framework establishes a robust digital twin ecosystem for real-time monitoring, predictive maintenance, and fleet-wide optimization. Comparative analysis reveals significant improvements in anomaly detection capabilities and reduced false alarm rates compared to traditional methods. The study conducts a systematic evaluation of the benefits and limitations of FM, FL, and integrated approaches in AHMS, examining their implications for system robustness, scalability, and security. Statistical analysis confirms that the integrated approach substantially enhances precision and recall in identifying potential failures while optimizing computational resources and training time. This paper outlines a detailed aviation ecosystem architecture integrating these advanced AI technologies across centralized processing, client, and communication domains. Future research directions are identified, focusing on improving model efficiency, ensuring generalization across diverse operational conditions, and addressing regulatory and ethical considerations.

Suggested Citation

  • Igor Kabashkin, 2024. "Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems," Mathematics, MDPI, vol. 12(21), pages 1-32, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3428-:d:1511914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3428/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/21/3428/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3428-:d:1511914. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.