Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems
This work addresses anomaly detection for resource-constrained edge IoT systems, but it is incremental as it applies an existing federated anomaly detection method to a specific application.
The paper tackles efficient anomaly detection on edge IoT systems by applying an Isolation Forest-based method within federated learning frameworks, achieving over 96% accuracy and above 78% precision in detecting anomalies with memory usage below 160 KB during training.
Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low-resource, unsupervised method well-suited for edge deployment and continuous learning. In this paper, we present an application of Isolation Forest-based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 96% accuracy in distinguishing normal from abnormal readings and above 78% precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 160 KB during model training. These results highlight its suitability for resource-constrained environments and edge systems, while upholding federated learning principles of data privacy and collaborative learning.