Decentralized AI on The Edge: Implementing Federated Learning for Predictive Maintenance in Industrial IoT Systems

Candra  Supriadi; Wiwid  Wahyudi; Agus  Priyadi; Kim So  Jin

doi:10.51903/jtie.v4i2.281

Authors

Candra Supriadi Universitas Sains dan Teknologi Komputer, Semarang, Indonesia
Wiwid Wahyudi Universitas Sains dan Teknologi Komputer, Semarang, Indonesia https://orcid.org/0009-0008-4517-3227
Agus Priyadi Universitas Sains dan Teknologi Komputer, Semarang, Indonesia https://orcid.org/0009-0007-7487-2249
Kim So Jin Chonnam National University, Gwangju, South Korea https://orcid.org/0009-0007-5483-523X

DOI:

https://doi.org/10.51903/jtie.v4i2.281

Keywords:

Facial Recognition, Deep Learning, Convolutional Neural Network, Real-Time Detection, Security Systems

Abstract

The integration of Artificial Intelligence (AI) into Industrial Internet of Things (IIoT) systems has enhanced predictive maintenance strategies by enabling early detection of faults in machinery. However, centralized AI models often face challenges related to data privacy, latency, and communication overhead in industrial environments. This study aims to develop a decentralized AI framework utilizing Federated Learning (FL) on edge devices to enhance predictive maintenance in a medium-scale manufacturing plant. The proposed system enables local edge nodes to collaboratively train machine learning models without sharing raw data, thereby preserving data privacy and reducing network load. A prototype was developed using embedded edge devices integrated with vibration and temperature sensors to detect machine anomalies. Federated averaging was used to aggregate local models into a global model. Experimental results show that the federated model achieved 91.4% accuracy in anomaly detection, comparable to centralized approaches, while significantly reducing data transmission volume by 68%. This research demonstrates the feasibility of deploying federated learning on resource-constrained edge devices for predictive maintenance in IIoT environments. The findings suggest that decentralized AI at the edge can offer efficient, privacy-preserving, and scalable solutions for industrial applications

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