A Federated Learning Approach for Efficient Anomaly Detection in Electric Power Steering Systems

Electric power steering (EPS) has emerged as a valuable driver-assistance system. In an EPS system, an extensive amount of data collected from various sensors is analyzed to enhance the driving experience. Anomaly detection techniques have shown potential in ensuring the integrity of data patterns and detecting abnormalities to prevent adverse driving incidents, thus improving vehicle safety. However, traditional centralized anomaly detection methods require the collection of data from all EPS sensors, resulting in high communication network overhead. Herein, we propose an approach for anomaly detection using EPS data within a federated learning (FL) framework. Our approach exploits deep learning (DL) on time series data to achieve highly effective anomaly detection. This synergy of FL and DL enables all sensors in an EPS system to collaboratively train the anomaly detection model simultaneously. Our results demonstrate the feasibility of combining FL with DL anomaly detection in EPS systems, thus overcoming the limitations of the traditional centralized approach. The combined FL and DL model performs remarkably well, achieving an F1-Score of 99.01%, outperforming the 98.31%~98.89% achieved by the centralized approach. It also exhibits high generalizability by incorporating insights from all sensors to comprehensively understand diverse driving scenarios. This results in a significant reduction in error rates, ranging from 20% to 33.25%, compared to centralized methods. Additionally, the proposed model exhibits significant advantages over traditional methods, including reduced training time and communication overhead, while maintaining comparable anomaly detection accuracy performance.