4 citations · 5 across the 14 of their papers we have counts for
8 papers · 1 filter
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study
Yulin Fei, Yuhui Gao, Xingyuan Xian +3
With the rise of multimodal large language models, accurately extracting and understanding textual information from video content, referred to as video based optical character reco…
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
Haoyang Li, Wei Chen, Xiaojin Zhang
Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate t…
RSL-SQL: Robust Schema Linking in Text-to-SQL Generation
Zhenbiao Cao, Yuanlei Zheng, Zhihao Fan +3
Text-to-SQL generation aims to translate natural language questions into SQL statements. In Text-to-SQL based on large language models, schema linking is a widely adopted strategy…
Mitigating Data Reconstruction Attacks in Federated Learning: Conditions, Bounds, and Theoretical Guarantees
Xiaojin Zhang, Wei Chen, Mingcong Xu +1
Privacy attacks, particularly data reconstruction attacks, pose significant challenges to federated learning despite its promise in privacy-preserving collaborative training. This…
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
Xiaojin Zhang, Mingcong Xu, Wei Chen
In this paper, we first give an introduction to the theoretical basis of the privacy-utility equilibrium in federated learning based on Bayesian privacy definitions and total varia…
No Free Lunch Theorem for Privacy-Preserving LLM Inference
Xiaojin Zhang, Yahao Pang, Yan Kang +4
Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productiv…
Bridging Privacy and Robustness for Trustworthy Machine Learning
Xiaojin Zhang, Wei Chen
The widespread adoption of machine learning necessitates robust privacy protection alongside algorithmic resilience. While Local Differential Privacy (LDP) provides foundational gu…
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
Jialuo He, Wei Chen, Xiaojin Zhang
Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical het…