针对工业系统故障检测难题，提出增强型联邦学习框架。
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems
- 按传感器可靠性动态聚合模型，提升训练鲁棒性。
- 在节点故障下仍保持99.5%异常检测AUC-ROC性能。
- 适合对可靠性与效率要求高的工业物联网场景。
随着工业网络物理系统（CPS）复杂度提升，异常检测与定位面临传感器可靠性差异和节点失效等挑战。现有联邦学习（FL）方法缺乏针对这些特性的应对机制。本文提出一种增强型联邦学习框架，包含三项创新：基于传感器可靠性的自适应模型聚合、面向资源优化的动态节点选择、基于威布尔分布的检查点容错机制。在NASA轴承与液压系统数据集上的实验表明，该框架优于现有最优FL方法，在各类工况下实现99.5% AUC-ROC异常检测性能，并在节点故障时维持精度。通过曼-惠特尼U检验（p<0.05）验证，检测准确率与计算效率均有显著提升。
原文摘要 · Abstract (English)
Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability and node failures in distributed environments. While federated learning (FL) provides a foundation for distributed model training, existing approaches often lack mechanisms to address these CPS-specific challenges. This paper introduces an enhanced FL framework with three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. The proposed framework ensures reliable condition monitoring while tackling the computational and reliability challenges of industrial CPS deployments. Experiments on the NASA Bearing and Hydraulic System datasets demonstrate superior performance compared to state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy even under node failures. Statistical validation using the Mann-Whitney U test confirms significant improvements, with a p-value less than 0.05, in both detection accuracy and computational efficiency across various operational scenarios.
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