2 papers
cs.CR2026
Safe-FedLLM: Delving into the Safety of Federated Large Language Models
Mingxiang Tao, Yu Tian, Wenxuan Tu +3
Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on improving the efficiency of federated lea…
cs.CV2021
Foreground Object Structure Transfer for Unsupervised Domain Adaptation
Jieren Cheng, Le Liu, Xiangyan Tang +5
Unsupervised domain adaptation aims to train a classification model from the labeled source domain for the unlabeled target domain. Since the data distributions of the two domains…