5 papers
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
Wentao Yu, Bo Han, Jie Yang +1
Graph Federated Learning (GFL) enables collaborative representation learning across distributed subgraphs while preserving privacy. However, heterogeneity remains a critical challe…
Graph Federated Unlearning for Privacy Preservation
Ruotong Ma, Wentao Yu, Qizhou Wang +2
Graph federated learning (GFL) facilitates decentralized training on distributed graph data while keeping sensitive user information local, aligning with policies such as GDPR and…
Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
Wentao Yu, Sheng Wan, Shuo Chen +2
Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from di…
Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective
Wentao Yu
Since heterogeneity presents a fundamental challenge in graph federated learning, many existing methods are proposed to deal with node feature heterogeneity and structure heterogen…
Modeling Inter-Intra Heterogeneity for Graph Federated Learning
Wentao Yu, Shuo Chen, Yongxin Tong +2
Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the h…