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REPTILE: a Tool for Replay-driven Continual Learning in IIoT

Abstract

We present an automated Machine Learning (ML) tool designed as a continual learning pipeline to adapt to evolving data streams in the Industrial Internet of Things (IIoT). This tool creates ML experiences, starting with training a neural network model. It then iteratively refines this model using fresh data while judiciously replaying pertinent historical data segments. When applied to IIoT sensor data, our tool ensures sustained ML performance amid evolving data dynamics while preventing the undue accumulation of obsolete sensor data. We have successfully assessed our tool across three industrial datasets and affirm its efficacy in dynamic knowledge retention and adaptation.
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Category

Academic chapter

Language

English

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies

Year

2024

Publisher

Association for Computing Machinery (ACM)

Book

IoT '23: Proceedings of the 13th International Conference on the Internet of Things

ISBN

9798400708541

Page(s)

204 - 207

View this publication at Norwegian Research Information Repository