HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
<|reference_start|>HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning: 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.<|reference_end|>
@article{guo2024hifgl:,
    title={HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated
  Graph Learning},
    author={Zhuoning Guo, Duanyi Yao, Qiang Yang, Hao Liu},
    journal={arXiv preprint arXiv:2406.10616},
    year={2024},
    doi={10.1145/3637528.3671660},
    archivePrefix={arXiv},
    eprint={2406.10616},
    primaryClass={cs.LG cs.DC}
}