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Related Experiment Videos

BFLAFD: blockchain-enabled federated learning framework for adaptive fire detection in IIoT networks.

Jayameena Desikan1, Sushil Kumar Singh2, A Jayanthiladevi3

  • 1Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, India.

Scientific Reports
|May 5, 2026
PubMed
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A new Blockchain-assisted Federated Learning framework for Adaptive Fire Detection (BFLAFD) enhances Industrial Internet of Things (IIoT) security. This solution provides accurate, real-time fire detection for critical sectors like oil and gas.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Industrial Internet of Things (IIoT)

Background:

  • The Industrial Internet of Things (IIoT) in oil and gas sectors faces fire detection challenges: unreliable sensor data, privacy concerns, communication delays, and lack of generalized models.
  • Centralized fire detection methods in IIoT environments are insufficient due to data confidentiality issues and communication inefficiencies.

Purpose of the Study:

  • To introduce a novel Blockchain-assisted Federated Learning framework for Adaptive Fire Detection (BFLAFD) to address IIoT fire detection limitations.
  • To enhance fire detection accuracy, security, and efficiency in distributed IIoT environments, specifically within the oil and gas industry.

Main Methods:

  • Utilized Federated Learning (FL) for on-device model training, preserving data confidentiality and reducing data transmission.
Keywords:
BlockchainFederated Learning (FL)Fire DetectionIndustrial Internet of Things (IIoT)Personalized Federated Learning (PFL)

Related Experiment Videos

  • Implemented a hierarchical aggregation process for optimized global model performance, addressing sensor drift and device heterogeneity.
  • Integrated a permissioned blockchain with smart contracts for secure access control, log transparency, and tamper-proof model integrity. Employed Personalized Federated Learning (PFL) for customized models.

    • Main Results:

    • BFLAFD achieved 98.2% fire detection accuracy with a 2.7% false alarm rate.
    • Inference latency was recorded between 100-150 ms, with blockchain validation at 1-2 seconds.
    • Communication costs were reduced by 82.3% compared to centralized training methods.

    Conclusions:

    • BFLAFD provides a robust, secure, and efficient solution for fire detection in critical IIoT environments.
    • The framework successfully overcomes challenges of data reliability, privacy, and model generalization in distributed industrial settings.