Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study

TL;DR

This paper demonstrates that federated learning can effectively perform anomaly detection in resource-constrained IIoT environments using LoRaWAN, achieving high accuracy and privacy preservation through optimized autoencoder models.

Contribution

It introduces a federated learning framework tailored for LoRaWAN-enabled IIoT systems, optimizing autoencoder neural networks for anomaly detection in industrial machinery.

Findings

FL achieves 94.77% F1 score, comparable to centralized models.

FL requires minimal message transmission, suitable for LoRaWAN constraints.

Optimal training configurations identified for practical deployment.

Abstract

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.