3 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang +2
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of cl…
cs.LG2022★ 2 cited
FadMan: Federated Anomaly Detection across Multiple Attributed Networks
Nannan Wu, Ning Zhang, Wenjun Wang +2
Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an incre…
cs.DS2021★ 1 cited
ANOMALYMAXQ:Anomaly-Structured Maximization to Query in Attributed Network
Xinyue Zhang, Nannan Wu, Zixu Zhen +1
The detection of anomaly subgraphs naturally appears in various real-life tasks, yet label noise seriously interferes with the result. As a motivation for our work, we focus on ina…