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

Conference ·
 [1];  [1];  [1]
  1. University of Texas at El Paso,Department of Computer Science,El Paso,USA
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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) offers a foundation for distributed model training, existing approaches lack mechanisms to handle these CPS-specific challenges. This paper presents an enhanced FL framework that introduces three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. Our framework enables reliable condition monitoring while addressing the computational and reliability challenges of industrial CPS deployments. Experiments on NASA Bearing and Hydraulic System Datasets demonstrate superior performance over state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy under node failures. Statistical validation using Mann-Whitney (U) test confirms significant improvements (p < 0.05) in both detection accuracy and computational efficiency across diverse operational scenarios.1

Research Organization:
National Energy Technology Laboratory
Sponsoring Organization:
Office of Fossil Energy; US Department of Energy
DOE Contract Number:
FE0032089
OSTI ID:
2583898
Country of Publication:
United States
Language:
English

References (12)

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Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid journal November 2024
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Maximum Dynamic Errors of Elliptic Low-Pass and Band-Pass Filters in Control and Monitoring Systems conference March 2024
Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin conference June 2024
Condition monitoring of a complex hydraulic system using multivariate statistics conference May 2015
End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems journal January 2024
Condition monitoring and anomaly detection in cyber-physical systems conference June 2022
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning conference August 2023
Checkpointing Strategies to Tolerate Non-Memoryless Failures on HPC Platforms journal March 2024
Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation journal January 2024

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