用压缩图替代参数通信，提升联邦图学习效率与隐私性
Rethinking Federated Graph Learning: A Data Condensation Perspective
- 用凝聚图作为优化载体，替代传统参数传输
- 在6个数据集上优于现有方法，收敛更快且通信量更低
- 适合注重隐私与通信效率的分布式图学习场景
联邦图学习通过多客户端图协作训练图神经网络，但现有方法依赖模型参数或梯度通信，难以应对复杂多样的图分布带来的数据异质性。部分方法虽尝试共享额外信息以提升收敛速度，却带来显著隐私风险和通信开销。为此，本文提出凝聚图概念，构建新型联邦图学习范式FedGM。通过广义凝聚图共识机制，从分布式图中聚合全面知识，仅需一次传输凝聚数据即可实现高效聚合，显著降低通信成本与隐私风险。在六个公开数据集上的大量实验表明，FedGM consistently优于现有先进基线，展现出作为新型联邦图学习范式的巨大潜力。
原文摘要 · Abstract (English)
Federated graph learning is a widely recognized technique that promotes collaborative training of graph neural networks (GNNs) by multi-client graphs.However, existing approaches heavily rely on the communication of model parameters or gradients for federated optimization and fail to adequately address the data heterogeneity introduced by intricate and diverse graph distributions. Although some methods attempt to share additional messages among the server and clients to improve federated convergence during communication, they introduce significant privacy risks and increase communication overhead. To address these issues, we introduce the concept of a condensed graph as a novel optimization carrier to address FGL data heterogeneity and propose a new FGL paradigm called FedGM. Specifically, we utilize a generalized condensation graph consensus to aggregate comprehensive knowledge from distributed graphs, while minimizing communication costs and privacy risks through a single transmission of the condensed data. Extensive experiments on six public datasets consistently demonstrate the superiority of FedGM over state-of-the-art baselines, highlighting its potential for a novel FGL paradigm.
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