Building Accurate and Interpretable Online Classifiers on Edge Devices

By integrating machine learning with edge devices, we can augment the capabilities of edge devices, such as IoT devices, household appliances, and wearable technologies. These edge devices generally operate on microcontrollers with inherently limited resources, such as constrained RAM capacity and l...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 36; no. 8; pp. 1779 - 1796
Main Authors: Zhang, Yuanming, Wang, Pinghui, Cheng, Kuankuan, Zhao, Junzhou, Tao, Jing, Hai, Jingxin, Feng, Junlan, Deng, Chao, Wang, Xidian
Format: Journal Article
Language:English
Published: IEEE 01.08.2025
Subjects:
Accuracy
Computational modeling
Data models
Edge computing
GSM
Image edge detection
Kernel
machine-learning kernel
Memory management
Monitoring
online learning
Random access memory
resource-constrained device
sketching algorithm
Training
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:By integrating machine learning with edge devices, we can augment the capabilities of edge devices, such as IoT devices, household appliances, and wearable technologies. These edge devices generally operate on microcontrollers with inherently limited resources, such as constrained RAM capacity and limited computational power. Nonetheless, they often process data in a high-velocity stream fashion, exemplified by sequences of activities and statuses monitored by advanced industrial sensors. In practical scenarios, models must be interpretable to facilitate troubleshooting and behavior understanding. Implementing machine learning models on edge devices is valuable and challenging, striking a balance between model efficacy and resource constraint. To address this challenge, we introduce our novel Onfesk, which combines online learning algorithms with an innovative interpretable kernel. Specifically, our Onfesk trains an online classifier over the kernel's feature sketches. Benefiting from our specially designed modules, the kernel's feature sketches can be efficiently produced, and the memory requirements of the classifier can be significantly reduced. As a result, Onfesk delivers effective and efficient performance in environments with limited resources without compromising on model interpretability. Extensive experiments with diverse real-world datasets have shown that Onfesk outperforms state-of-the-art methods, achieving up to a 7.4% improvement in accuracy within identical memory constraints.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2025.3579121