@inproceedings{
pan2023fedgkd,
title={Fed{GKD}: Unleashing the Power of Collaboration in Federated Graph Neural Networks},
author={Qiying Pan and Ruofan Wu and Tengfei LIU and Tianyi Zhang and Yifei Zhu and Weiqiang Wang},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=qCgptbOsdS}
}
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Qiying Pan Ruofan Wu Tengfei LIU Tianyi Zhang Yifei Zhu Weiqiang Wang