4 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…
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…
Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
Yinlin Zhu, Di Wu, Xianzhi Zhang +4
Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models, an…
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…