6 papers
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xunkai Li, Meihao Liao, Zhengyu Wu +4
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…
Towards Effective and General Graph Unlearning via Mutual Evolution
Xunkai Li, Yulin Zhao, Zhengyu Wu +3
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving…
FedGTA: Topology-aware Averaging for Federated Graph Learning
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL s…
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break dat…