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<p>Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.</p>

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Privacy-preserving predictive maintenance method for cross-border unmanned logistics system integrating federated learning and blockchain

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Qingzhen Meng

摘要

Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.