3 papers
cs.CR2025
EASE: Practical and Efficient Safety Alignment for Small Language Models
Haonan Shi, Guoli Wang, Tu Ouyang +1
Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct…
cs.CR2025
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
Haonan Shi, Tu Ouyang, An Wang
Federated Distillation (FD) has emerged as a popular federated training framework, enabling clients to collaboratively train models without sharing private data. Public Dataset-Ass…
cs.LG2025
Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models
Haonan Shi, Tu Ouyang, An Wang
Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks. However, real-world fine-tuning faces inherent conflicts between…