# Link-Aware Energy-Frugal Continual Learning for Fault Detection in IoT Networks

arXiv: https://arxiv.org/abs/2512.13340 · HTML: https://mlsanity.com/paper/2512.13340/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Pruning vs Quantization: Which is Better?](https://mlsanity.com/paper/2307.02973/) (2023)
- [Minimum Energy Quantized Neural Networks](https://mlsanity.com/paper/1711.00215/) (2017)
- [Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information](https://mlsanity.com/paper/1907.09204/) (2019)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Reconstruction Error-based Anomaly Detection with Few Outlying Examples](https://mlsanity.com/paper/2305.10464/) (2023)
- [TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices](https://mlsanity.com/paper/2311.04788/) (2023)

## Built on by (most cited first, 1 of 1)

- [Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts](https://mlsanity.com/paper/2603.07507/) (2026)

## Abstract (arXiv)

The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates due to the non-stationarity in the IoT environment and limited initial training data. To counteract this, the deployed models can be updated occasionally with new observed data samples. However, this approach consumes additional energy, which is undesirable for energy constrained IoT devices. This letter introduces an event-driven communication framework that strategically integrates continual learning (CL) in IoT networks for energy-efficient fault detection. Our framework enables the IoT device and the edge server (ES) to collaboratively update the lightweight ML model by adapting to the wireless link conditions for communication and the available energy budget. Evaluation on real-world datasets show that the proposed approach can outperform both periodic sampling and non-adaptive CL in terms of inference recall; our proposed approach achieves up to a 42.8% improvement, even under tight energy and bandwidth constraint.