3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.DB2024★ 3 cited
A Comprehensive Study of Shapley Value in Data Analytics
Hong Lin, Shixin Wan, Zhongle Xie +4
Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). This paper presents the first co…
cs.LG2024★ 1 cited
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
Hong Lin, Lidan Shou, Ke Chen +2
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal feder…
cs.LG2020
LINDT: Tackling Negative Federated Learning with Local Adaptation
Hong Lin, Lidan Shou, Ke Chen +2
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without dis…