HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning

This is the official repository of the KDD'2024 Research Track Paper "HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning" (Preprint).

Introduction

Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable efforts have been made for FGL under either cross-device or cross-silo paradigm, how to effectively capture graph knowledge in a more complicated cross-silo cross-device environment remains an under-explored problem. However, this task is challenging because of the inherent hierarchy and heterogeneity of decentralized clients, diversified privacy constraints in different clients, and the cross-client graph integrity requirement. To this end, in this paper, we propose a Hierarchical Federated Graph Learning (HiFGL) framework for cross-silo cross-device FGL. Specifically, we devise a unified hierarchical architecture to safeguard federated GNN training on heterogeneous clients while ensuring graph integrity. Moreover, we propose a Secret Message Passing (SecMP) scheme to shield unauthorized access to subgraph-level and node-level sensitive information simultaneously. Theoretical analysis proves that HiFGL achieves multi-level privacy preservation with complexity guarantees. Extensive experiments on real-world datasets validate the superiority of the proposed framework against several baselines. Furthermore, HiFGL's versatile nature allows for its application in either solely cross-silo or cross-device settings, further broadening its utility in real-world FGL applications.

Requirements

• Python 3.8
• PyTorch 1.10
• PyTorch Lightning 1.5.3
• TorchMetrics 0.6

Data

We process data from traditional graph datasets, the processed data are provided at Google Drive HiFGL-KDD2024-Data. Please download and unzip it and put the folder in the first level of the whole repository (i.e., parallel with code folder).

Run

An example that appoints dataset with cora and epochs with 64.

python ./code/main.py -D cora -E 64

Citation

@article{guo2024hifgl,
  title={HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning},
  author={Guo, Zhuoning and Yao, Duanyi and Yang, Qiang and Liu, Hao},
  journal={arXiv preprint arXiv:2406.10616},
  year={2024}
}

@inproceedings{10.1145/3637528.3671660,
  author = {Guo, Zhuoning and Yao, Duanyi and Yang, Qiang and Liu, Hao},
  title = {HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning},
  year = {2024},
  isbn = {9798400704901},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3637528.3671660},
  doi = {10.1145/3637528.3671660},
  abstract = {},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {968–979},
  numpages = {12},
  keywords = {federated graph learning, graph neural network, multi-level privacy preservation},
  location = {Barcelona, Spain},
  series = {KDD '24}
}