6 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…
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…
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…
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
Xiaojin Zhang, Wei Chen
Federated learning has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, recent studies have shown that it is vulnerable to v…
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…