Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems
Pavle Vasiljevic, Milica Matic, Miroslav Popovic

TL;DR
This paper presents a federated Isolation Forest-based anomaly detection system tailored for resource-constrained edge IoT devices, achieving high accuracy and low memory usage while preserving data privacy.
Contribution
It introduces a novel federated anomaly detection approach using Isolation Forests optimized for MicroPython-enabled edge devices, demonstrating practical effectiveness.
Findings
Over 96% accuracy in anomaly detection
Above 78% precision across configurations
Memory usage below 160 KB during training
Abstract
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…
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