Enabling On-Tiny-Device Model Personalization via Gradient Condensing and Alternant Partial Update

On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous, on-device training is increasingly essential since users can benefit from the personalized model without transmitting data and model parameters to th...

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Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Jia, Zhenge, Shi, Yiyang, Bao, Zeyu, Wang, Zirui, Pang, Xin, Liu, Huiguo, Duan, Yu, Shen, Zhaoyan, Zhao, Mengying
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Accuracy Adaptation models Backpropagation Computational modeling Costs Data models Performance evaluation Servers Strain Training
Online Access:	Get full text
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