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

Sohail Sayed () and Nauman Sayed ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 08, 2304-2312

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].

Keywords: Federated Learning; Differential Privacy; Homomorphic Encryption; Secure Aggregation; Supply Chain Risk Management; Supply Chain Resilience; Collaborative AI. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:08:ijisrt26aug1100

DOI: 10.38124/ijisrt/26aug1100

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