Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning-Reference-Cited by-同舟云学术

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Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning

Published:2024 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Kong Xiangjie¹^ORCID,Zhang Wenyi¹^ORCID,Wang Hui²^ORCID,Hou Mingliang²^ORCID,Chen Xin²^ORCID,Yan Xiaoran³^ORCID,Das Sajal K.⁴^ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

2. School of Software, Dalian University of Technology, Dalian, China

3. Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China

4. Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Zhejiang Lab

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/5962385/6104215/10566052.pdf?arnumber=10566052

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1. BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks;Sensors;2024-07-15

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