Advancing privacy-aware machine learning on sensitive data via edge-based continual µ-training for personalized large models

Advancing privacy-aware machine learning on sensitive data via edge-based continual µ-training for personalized large models

This paper introduces an innovative method for fine-tuning a large multi-label model for abnormality detection, utilizing a smaller trainer and advanced knowledge distillation techniques. It studies the effects of fine-tuning on various abnormalities, noting different improvements based on the Origi...

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Bibliographic Details
Main Authors: Zhaojing Huang, Leping Yu, Luis Fernando Herbozo Contreras, Kamran Eshraghian, Nhan Duy Truong, Armin Nikpour, Omid Kavehei
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
knowledge distillation
edged device
fine-tuning
transfer learning
heart abnormalities
cardiovascular diseases
Online Access:https://doi.org/10.1088/2632-2153/adaca3
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https://doi.org/10.1088/2632-2153/adaca3

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