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This work aims to develop an approach for maintaining knowledge retention in compact AI models while deploying in edge environments.

H-LoRA: Rethinking Rank Selection for Controllable Knowledge Retention in Edge AI

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This work aims to develop an approach for maintaining knowledge retention in compact AI models while deploying in edge environments.
Introduced H-LoRA for high-rank adaptation in edge models.
Conducted experiments on compact models like Minimind and Qwen across three domains.
Evaluated knowledge retention and performance metrics with a sample size of 29,647 across diverse tasks.
H-LoRA demonstrated 90% topic retention compared to 1% with traditional SFT.
Achieved SFT-level precision of 99.81% with significantly fewer trainable parameters (20.35%).
Reduced OTA update size from 1.4 GB to 96 MB, enhancing deployment efficiency.

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Randomized trial shows improved knowledge retention in edge AI models, indicating better deployment outcomes.

Lun et al. (2026) studied this question.

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