HSDC-Net: Privacy-Preserving and Trust-Aware Historical-State Decay Compensation for Secure Distributed Industrial Edge Monitoring

INTRODUCTION: Distributed industrial edge monitoring requires long-sequence models to remain reliable under non-stationary operating conditions while preventing sensitive local data from being exposed through cross-node historical-state sharing. OBJECTIVES: This work addresses secure reuse of historical representations under feature drift, privacy leakage, and poisoned-memory propagation. METHODS: We propose HSDC-Net, which locally encodes heterogeneous process streams, releases clipped and differentially private latent anchors, estimates cross-node inconsistency and recursive source trust, aligns reliable historical states through low-rank context transport, and restricts correction to a learned nuisance-drift subspace. RESULTS: Experiments on four industrial datasets show that HSDC-Net consistently improves predictive performance and drift robustness with moderate online latency. It also suppresses malicious-anchor acceptance while maintaining a finite privacy budget with only limited utility degradation relative to the non-private variant. CONCLUSION: HSDC-Net provides a unified privacy-aware and poisoning-resilient mechanism for robust distributed industrial monitoring without centralizing raw manufacturing streams across heterogeneous monitoring and maintenance tasks.