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

arXiv: https://arxiv.org/abs/2203.01077 · HTML: https://mlsanity.com/paper/2203.01077/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices](https://mlsanity.com/paper/1907.10147/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition](https://mlsanity.com/paper/2408.01283/) (2024)
- [Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices](https://mlsanity.com/paper/2410.21073/) (2024)
- [A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices](https://mlsanity.com/paper/2212.09637/) (2022)
- [Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation](https://mlsanity.com/paper/2408.05617/) (2024)
- [PRIOT: Pruning-Based Integer-Only Transfer Learning for Embedded Systems](https://mlsanity.com/paper/2503.16860/) (2025)
- [InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models](https://mlsanity.com/paper/2506.06505/) (2025)

## Abstract (arXiv)

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.