1 citations · 1 across the 4 of their papers we have counts for
5 papers
Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li +2
Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy. However, real-world data categories frequently exhibit l…
Generalized Category Discovery in Federated Graph Learning
Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +2
Federated Graph Learning (FGL) enables collaborative learning over distributed graph data, yet existing approaches largely rely on a closed-world assumption, limiting their applica…
DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li +3
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized a…
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +3
Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple local systems. However, most ex…
Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis
Qiang Qiao, Wenyu Wang, Meixia Qu +3
The field of medical image segmentation is challenged by domain generalization (DG) due to domain shifts in clinical datasets. The DG challenge is exacerbated by the scarcity of me…