用联邦学习在低功耗物联网中实现工业设备异常检测，效果接近中心化模型。
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
- 基于优化自编码器的联邦学习框架，支持分布式训练。
- 平均F1达94.77%，准确率92.30%，训练消息空中时长52.8分钟。
- 为资源受限工业场景提供可落地的参数配置指南。
智能工业互联网（IIoT）系统有望革新运维模式，提升效率。异常检测在预防性维护中至关重要。然而，传统机器学习受限于LoRaWAN等资源受限环境的消息与计算能力，难以部署。联邦学习（FL）通过分布式训练缓解隐私问题并减少数据传输，本研究探索其在使用IIoT原型与LoRaWAN通信的工业及建筑机械架构中的异常检测应用。采用优化自编码器结构，对比联邦与集中式模型。尽管客户端数据分布不均，联邦学习仍表现良好：平均F1得分94.77%，准确率92.30%，真阴率（TNR）90.65%，真阳率（TPR）92.93%，训练消息空中时长52.8分钟。各设备本地评估显示模型具备适应性。分析还确定了消息需求、最低训练时长及最优轮次/周期配置，为未来在受限工业环境中的实施提供指导。
原文摘要 · Abstract (English)
The development of intelligent Industrial Internet of Things (IIoT) systems promises to revolutionize operational and maintenance practices, driving improvements in operational efficiency. Anomaly detection within IIoT architectures plays a crucial role in preventive maintenance and spotting irregularities in industrial components. However, due to limited message and processing capacity, traditional Machine Learning (ML) faces challenges in deploying anomaly detection models in resource-constrained environments like LoRaWAN. On the other hand, Federated Learning (FL) solves this problem by enabling distributed model training, addressing privacy concerns, and minimizing data transmission. This study explores using FL for anomaly detection in industrial and civil construction machinery architectures that use IIoT prototypes with LoRaWAN communication. The process leverages an optimized autoencoder neural network structure and compares federated models with centralized ones. Despite uneven data distribution among machine clients, FL demonstrates effectiveness, with a mean F1 score (of 94.77), accuracy (of 92.30), TNR (of 90.65), and TPR (92.93), comparable to centralized models, considering airtime of trainning messages of 52.8 min. Local model evaluations on each machine highlight adaptability. At the same time, the performed analysis identifies message requirements, minimum training hours, and optimal round/epoch configurations for FL in LoRaWAN, guiding future implementations in constrained industrial environments.
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