IDEAS home Printed from https://ideas.repec.org/a/cvr/ijisrt/202608ijisrt26aug1100.html

FedSCORE-PP: A Federated and PrivacyPreserving Machine Learning Framework for Collaborative Supply Chain Risk Prediction Across Organizations

Author

Listed:
  • Sohail Sayed

  • Nauman Sayed

Abstract

Global supply chains are increasingly exposed to disruptions whose effects propagate across organizational boundaries, yet the data needed to predict such risks is fragmented among firms reluctant to share it for competitive, contractual, and regulatory reasons. Centralized machine learning therefore under-utilizes collective evidence, and organizations with inadequate datasets cannot predict risk reliably on their own [1].

Suggested Citation

  • Sohail Sayed & Nauman Sayed, 2026. "FedSCORE-PP: A Federated and PrivacyPreserving Machine Learning Framework for Collaborative Supply Chain Risk Prediction Across Organizations," International Journal of Innovative Science and Research Technology (IJISRT), IJISRT Publication, vol. 11(08), pages 2304-2312, September.
    • Handle: RePEc:cvr:ijisrt:2026:08:ijisrt26aug1100
    DOI: https://doi.org/10.38124/ijisrt/26aug1100
    as

    Download full text from publisher

    File URL: https://www.ijisrt.com/fedscorepp-a-federated-and-privacypreserving-machine-learning-framework-for-collaborative-supply-chain-risk-prediction-across-organizations
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.38124/ijisrt/26aug1100?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:cvr:ijisrt:2026:08:ijisrt26aug1100. 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: Rahul Goyel (email available below). General contact details of provider: https://www.ijisrt.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.