6 citations · 16 across the 7 of their papers we have counts for
6 papers · 1 filter
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Yihao Li, Yanyi Lai, Chuan Chen +1
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been d…
Subspace-Contrastive Multi-View Clustering
Fu Lele, Zhang Lei, Yang Jinghua +3
Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information…
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
Taolin Zhang, Chuan Chen, Yaomin Chang +2
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some prac…
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Chuan Chen, Weibo Hu, Ziyue Xu +1
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are design…
An Uncoupled Training Architecture for Large Graph Learning
Dalong Yang, Chuan Chen, Youhao Zheng +2
Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) is an inherently coupled training framework repetitively conducting…
Collaborative Deep Learning Across Multiple Data Centers
Kele Xu, Haibo Mi, Dawei Feng +4
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter da…