Papers by Shuangping Huang
LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)
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| Challenge: | Existing approaches to fine-tune large language models are infeasible due to privacy regulations. |
| Approach: | They propose an offsite tuning framework that secures data privacy and model parameter and capability privacy. |
| Outcome: | The proposed framework secures data privacy and model parameter and capability privacy while preserving gradient alignment. |