基于K-Means的轻量级异常检测，实现设备间模型共享。
K-Means Based TinyML Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)
- 在MCU上用真实功耗数据做特征提取与聚类
- 训练一次模型，多设备直接复用，推理时间一致
- 适合资源受限的物联网设备群部署
本文提出一种面向资源受限微控制器（MCU）的轻量级K-Means异常检测模型及分布式模型共享流程。基于小型冰箱的实际功耗数据，系统在设备端完成特征提取、聚类与阈值估计，识别异常行为。为避免每台设备重复训练，提出分布式学习互联网（DIoL），将模型以文本形式导出并直接复用于其他设备。两设备原型验证了“训练一次，处处使用”（TOSE）的可行性：设备A训练模型，设备B无需重训即可推理。实验表明，异常检测行为一致，解析开销可忽略，独立运行与DIoL模式下的推理时间相同。该框架支持嵌入式设备集群的可扩展、低成本TinyML部署。
原文摘要 · Abstract (English)
This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feature extraction, clustering, and threshold estimation to identify abnormal appliance behavior. To avoid retraining models on every device, we introduce the Distributed Internet of Learning (DIoL), which enables a model trained on one MCU to be exported as a portable, text-based representation and reused directly on other devices. A two-device prototype demonstrates the feasibility of the "Train Once, Share Everywhere" (TOSE) approach using a real-world appliance case study, where Device A trains the model and Device B performs inference without retraining. Experimental results show consistent anomaly detection behavior, negligible parsing overhead, and identical inference runtimes between standalone and DIoL-based operation. The proposed framework enables scalable, low-cost TinyML deployment across fleets of embedded devices.
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