TY  - JOUR
AU  - Lun, Darren Chai Xin
AU  - Ming, Lim Tong
PY  - 2026
TI  - H-LoRA: Rethinking Rank Selection for Controllable Knowledge Retention in Edge AI
JO  - Computers, Materials & Continua
SN  - 1546-2218
VL  - 88
IS  - 1
AB  - The deployment of specialized language models in resource-constrained edge environments ( ≤1B parameters,  ≤2 GB memory,  ≤100 ms latency) faces a critical challenge: Supervised Fine-Tuning (SFT) achieves domain expertise but suffers from irreversible catastrophic forgetting, while traditional Low-Rank Adaptation (LoRA) with conservative ranks ( r ≤ 64) often underperforms due to insufficient adaptation capacity. This work introduces H-LoRA (High-Rank LoRA) for edge-deployable models and establishes a fundamental distinction between destructive forgetting and controllable knowledge retention. Through comprehensive experiments on compact models ( 0.12B Minimind and Qwen- 0.5B) across three domains (Human Resources, Medical, Mathematics) using 29,647 samples, we demonstrate that while both SFT and H-LoRA exhibit general capability degradation, they differ fundamentally: SFT completely destroys the original knowledge structure ( 1% topic retention), while H-LoRA maintains knowledge integrity with  90% topic retention—an  89 percentage point improvement—enabling post-deployment capability recovery. H-LoRA employs simplified scaling and strategic high-rank adaptation at approximately two-thirds of the model’s hidden dimension ( r = 512 for  d = 768), achieving SFT-level domain performance ( 99.81% precision) with  5× greater parameter efficiency ( 20.35% trainable parameters) and robust cross-domain generalization ( 93.5 ± 6.8% average precision). In addition, H-LoRA reduces over-the-air (OTA) update size from  1.4 GB to  96 MB ( ≈93%), enabling practical and frequent deployment of specialized models in bandwidth-limited edge environments. Beyond demonstrating effectiveness, this work establishes the first comprehensive framework for characterizing specialization-retention trade-offs in parameter-efficient fine-tuning, providing practical guidance for method selection in real-world deployments.
UR  - https://doi.org/10.32604/cmc.2026.080068
DO  - 10.32604/cmc.2026.080068