基于联邦学习的异常检测模型，适配微型物联网设备。
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems
- 用联邦隔离森林实现边缘设备无监督异常检测。
- 准确率超96%，异常检测精确率超78%，内存占用低于160KB。
- 适合资源受限的物联网场景，保护数据隐私且支持持续学习。
近年来，针对嵌入式系统中的用户隐私与效率问题，出现了如Python TestBed for Federated Learning Algorithms和MicroPython TestBed for Federated Learning Algorithms等联邦学习框架。更近地，一种基于隔离森林的高效联邦异常检测算法FLiForest被提出，具备低资源消耗、无监督特性，适用于边缘部署与持续学习。本文基于前述联邦学习框架，开发了一种基于隔离森林的温度异常检测应用，专为运行MicroPython的小型边缘设备和物联网系统设计。实验评估显示，该系统在所有测试配置下均实现了超过96%的正常/异常区分准确率，异常检测精确率高于78%，模型训练期间内存占用始终低于160 KB。结果表明，该方案在资源受限环境中具有高度适用性，同时符合联邦学习的数据隐私与协作学习原则。
原文摘要 · Abstract (English)
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.
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