Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems

TL;DR

This paper presents an advanced federated learning framework tailored for industrial cyber-physical systems, enhancing anomaly detection accuracy and robustness against node failures through adaptive aggregation, dynamic node selection, and Weibull-based checkpointing.

Contribution

It introduces novel FL techniques specifically designed for CPS challenges, improving reliability and efficiency in condition monitoring and anomaly detection.

Findings

Achieved 99.5% AUC-ROC in anomaly detection on NASA datasets.

Demonstrated robustness of the framework under node failures.

Statistically significant improvements in detection accuracy and efficiency.

Abstract

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.