18 papers
Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach
Yuhua Wang, Xiaodong Li, Yihao Guo +6
Federated prompt tuning (FPT) enables collaborative adaptation of vision--language models (VLMs) using lightweight prompts. Existing methods often address heterogeneity and privacy…
AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models
Zishuai Zhang, Hainan Zhang, Zhiming Zheng
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to generate hallucinations,…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infe…
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
Yuhua Wang, Qinnan Zhang, Xiaodong Li +6
Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses priv…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
VLMs have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. FL mitigates this…
Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
Qianchi Zhang, Hainan Zhang, Liang Pang +2
Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is known about how the order of retri…