9 papers
FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
Zhilin Liang, Yuxiang Wang, Zimu Zhou +3
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding generation in external knowledge to improve factuality and reduce hallucinations. Yet most d…
Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models
Yujing Wang, Jinwen Chen, Hainan Zhang +5
Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sensitive knowledge, posing risks o…
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…
ExpressMind: A Multimodal Pretrained Large Language Model for Expressway Operation
Zihe Wang, Yihuan Wang, Haiyang Yu. Zhiyong Cui +4
The current expressway operation relies on rule-based and isolated models, which limits the ability to jointly analyze knowledge across different systems. Meanwhile, Large Language…
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…
FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models
Zishuai Zhang, Hainan zhang, Weihua Li +4
Private data holds promise for improving LLMs due to its high quality, but its scattered distribution across data silos and the high computational demands of LLMs limit their deplo…