Addressing Gap between Training Data and Deployed Environment by On-Device Learning

• URL: http://arxiv.org/abs/2203.01077v4
• Date: Sun, 24 Dec 2023 19:05:00 GMT
• Title: Addressing Gap between Training Data and Deployed Environment by On-Device Learning
• Authors: Kazuki Sunaga, Masaaki Kondo, Hiroki Matsutani
• Abstract summary: This article introduces a neural network based on on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment.
• Score: 1.6258710071587594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. This article introduces its algorithm and implementation on wireless sensor nodes consisting of a Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment. The results also show that the ODL approach can save communication cost and energy consumption for battery-powered Internet of Things devices.

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