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<p>The Industrial Internet of Things (IIoT) presents significant challenges for training machine learning models due to data privacy concerns, heterogeneous data distributions, and limited bandwidth. This paper proposes HPoT (Hierarchical Proof-of-Trust), a novel federated learning-based consensus algorithm for IIoT infrastructure that addresses these limitations while ensuring data integrity and model fidelity. HPoT integrates blockchain technology with federated learning to create a decentralized, privacy-preserving framework enabling collaborative model training without raw data sharing. The algorithm features: (1) robust aggregation with weighted averaging and anomaly detection for heterogeneous datasets, (2) adaptive weighting prioritizing updates from trustworthy devices, (3) reputation scoring to detect malicious nodes, and (4) hierarchical three-tier architecture optimized for IIoT constraints. Using Byzantine fault tolerance principles, HPoT tolerates up to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\lfloor \frac{N}{3} \rfloor\)</EquationSource> </InlineEquation> malicious participants while maintaining convergence. Experimental evaluation demonstrates <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(70\%\)</EquationSource> </InlineEquation> reduction in communication rounds, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(50\%\)</EquationSource> </InlineEquation> decrease in per-node energy consumption, and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(75\%\)</EquationSource> </InlineEquation> lower consensus latency compared to traditional federated learning, while maintaining comparable accuracy (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(94.2\%\)</EquationSource> </InlineEquation> vs <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(94.8\%\)</EquationSource> </InlineEquation>). The algorithm effectively handles data heterogeneity, communication constraints, and adversarial attacks common in IIoT environments. HPoT provides a scalable, secure, and energy-efficient solution for privacy-preserving machine learning on resource-constrained IIoT devices, advancing practical deployment of collaborative AI in industrial settings.</p>

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Hierarchical proof of trust a Byzantine fault tolerant federated learning framework for industrial IoT applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Amit Chaurasia,
  • Sandeep Kumar Sharma,
  • Pramod Singh Rathore

摘要

The Industrial Internet of Things (IIoT) presents significant challenges for training machine learning models due to data privacy concerns, heterogeneous data distributions, and limited bandwidth. This paper proposes HPoT (Hierarchical Proof-of-Trust), a novel federated learning-based consensus algorithm for IIoT infrastructure that addresses these limitations while ensuring data integrity and model fidelity. HPoT integrates blockchain technology with federated learning to create a decentralized, privacy-preserving framework enabling collaborative model training without raw data sharing. The algorithm features: (1) robust aggregation with weighted averaging and anomaly detection for heterogeneous datasets, (2) adaptive weighting prioritizing updates from trustworthy devices, (3) reputation scoring to detect malicious nodes, and (4) hierarchical three-tier architecture optimized for IIoT constraints. Using Byzantine fault tolerance principles, HPoT tolerates up to \(\lfloor \frac{N}{3} \rfloor\) malicious participants while maintaining convergence. Experimental evaluation demonstrates \(70\%\) reduction in communication rounds, \(50\%\) decrease in per-node energy consumption, and \(75\%\) lower consensus latency compared to traditional federated learning, while maintaining comparable accuracy ( \(94.2\%\) vs \(94.8\%\) ). The algorithm effectively handles data heterogeneity, communication constraints, and adversarial attacks common in IIoT environments. HPoT provides a scalable, secure, and energy-efficient solution for privacy-preserving machine learning on resource-constrained IIoT devices, advancing practical deployment of collaborative AI in industrial settings.