4 citations · 4 across the 6 of their papers we have counts for
7 papers
Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers
Jinlin Liu, Wei Chen, Xiaojin Zhang
Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this issue, unlearnable examples introduce impe…
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
Xiaojin Zhang, Mingcong Xu, Yiming Li +2
Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses…
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Mingcong Xu, Xiaojin Zhang, Wei Chen +1
Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. Howeve…
FedEAT: A Robustness Optimization Framework for Federated LLMs
Yahao Pang, Xingyuan Wu, Xiaojin Zhang +2
Significant advancements have been made by Large Language Models (LLMs) in the domains of natural language understanding and automated content creation. However, they still face pe…
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…