3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 2 cited
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Lixu Wang, Yang Zhao, Jiahua Dong +5
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid…
cs.LG2023★ 3 cited
Exploring Federated Unlearning: Review, Comparison, and Insights
Yang Zhao, Jiaxi Yang, Yiling Tao +4
The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in feder…