26 citations · 38 across the 4 of their papers we have counts for
Showing cs.CRShow all
2 papers · 1 filter
cs.CR2026
Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning
Sheng Wan, Dashan Gao, Hanlin Gu +3
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among clients. Unlike traditional parameter-based FL methods that e…
cs.CR2020★ 12 cited
Privacy Threats Against Federated Matrix Factorization
Dashan Gao, Ben Tan, Ce Ju +2
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect suf…