1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems
Fengyuan Yu, Yuyuan Li, Xiaohua Feng +3
With the growing demand for safeguarding sensitive user information in recommender systems, recommendation attribute unlearning is receiving increasing attention. Existing studies…
cs.LG2024★ 1 cited
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
Xinting Liao, Weiming Liu, Chaochao Chen +7
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised…