14 citations · 16 across the 3 of their papers we have counts for
3 papers
cs.CR2023★ 14 cited
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Yuke Hu, Wei Liang, Ruofan Wu +5
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging fe…
cs.LG2023★ 1 cited
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
Ruofan Wu, Boqun Ma, Hong Jin +3
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling…
cs.LG2022★ 1 cited
sqSGD: Locally Private and Communication Efficient Federated Learning
Yan Feng, Tao Xiong, Ruofan Wu +2
Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We study FL under local notions of privacy constraints, which provides s…