Blockchain-Driven Federated Learning for Secure Industrial Predictive Maintenance

Md Hossain (), Nakul Bakchi (), Md Imran Hossain (), Suman Das () and Md Faysal Ahmed ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 06, 2442-2448

Abstract: Factories with similar machines could build better failure-prediction models by sharing data, but most do not want to share their raw records with competitors or a central server. Federated learning (FL) solves this problem by allowing each factory to keep its data locally while training a shared model. However, FL still depends on a central server and does not clearly track contributions. This paper shows a framework that combines FL with a lightweight permissioned ledger. Each factory trains a local model, signs its update, and stores only a hash of the update on the ledger. This provides data integrity, an audit trail, and controlled participation. Using the AI4I 2020 predictive-maintenance dataset across five simulated factories, the framework achieved a ROC-AUC of 0.949, close to the centralized model’s 0.974 and much better than a single factory’s 0.790. The ledger added only about 6% extra training time and required very little storage. This helps identify and remove malicious participants, making industrial data sharing more secure and trustworthy.

Keywords: Federated Learning; Blockchain; Predictive Maintenance; Industry 4.0; Industrial AI; Machine Learning Security. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:06:ijisrt26jun1585

DOI: 10.38124/ijisrt/26jun1585

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