通过共享谱知识与个性化偏好，提升跨域图学习的隐私保护性能
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
- 利用图的谱特性捕捉结构差异，共享通用谱知识
- 在跨数据集和跨域场景下，准确率最高提升12.3%
- 适合需要隐私保护的个性化图神经网络应用
个性化联邦图学习（pFGL）在不泄露隐私的前提下，实现图神经网络（GNNs）的分布式训练，并满足非独立同分布（non-IID）参与者的需求。在跨域场景中，结构异质性给pFGL带来显著挑战。现有方法错误地全局共享非通用知识，且在领域结构变化时无法本地化定制个性化解决方案。我们首次揭示图的谱特性能有效反映内在的领域结构偏移。据此，提出共享通用谱知识的新策略。同时，指出图结构消息传递存在偏差，引入个性化偏好模块。结合两者，提出新型框架FedSSP，实现通用谱知识共享与图偏好满足。在跨数据集和跨域设置下进行大量实验，验证其优越性。
原文摘要 · Abstract (English)
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://github.com/OakleyTan/FedSSP.
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