ASFGNN: Automated separated-federated graph neural network

• Published in: Peer-to-peer networking and applications Vol. 14; no. 3; pp. 1692 - 1704
• Main Authors: Zheng, Longfei, Zhou, Jun, Chen, Chaochao, Wu, Bingzhe, Wang, Li, Zhang, Benyu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Automation; Clients; Communications Engineering; Computer Communication Networks; Engineering; Graph neural networks; Information Systems and Communication Service; Learning; Mathematical models; Message passing; Networks; Neural networks; Optimization; Optimization techniques; Parameters; Signal,Image and Speech Processing; Special Issue on Privacy-Preserving Computing; Training; Tuning; Federated learning; Privacy preserving; Bayesian optimization; Graph neural network

Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relationships. However, in practice, such data are usually isolated by different data owners (clients) and thus are likely to be...