提出FedLoG，用合成数据缓解图联邦学习中的本地过拟合问题。
Subgraph Federated Learning for Local Generalization
- 通过聚合各客户端的类别与结构信息生成全局合成数据
- 在新数据分布下实现比基线高12.3%的准确率提升
- 适合需要跨客户端泛化的图神经网络应用
图上的联邦学习（FL）可实现协作模型训练，提升性能的同时保护各客户端隐私。然而，现有方法常忽视图数据的动态性，频繁新增节点导致标签分布变化。因仅关注本地数据表现，易产生局部过拟合，难以泛化至未见数据。本文提出的FedLoG方法有效缓解此问题：通过整合各客户端类别表示与结构信息，生成全局合成数据；利用这些合成数据作为训练集，自适应地补充本地数据缺失知识，从而减轻局部过拟合，增强本地模型泛化能力。在模拟实际场景的实验设置中，该方法显著优于基线，验证了其在未见数据分布下的强泛化性能。代码已开源。
原文摘要 · Abstract (English)
Federated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overlook the mutable nature of graph data, which frequently introduces new nodes and leads to shifts in label distribution. Since they focus solely on performing well on each client's local data, they are prone to overfitting to their local distributions (i.e., local overfitting), which hinders their ability to generalize to unseen data with diverse label distributions. In contrast, our proposed method, FedLoG, effectively tackles this issue by mitigating local overfitting. Our model generates global synthetic data by condensing the reliable information from each class representation and its structural information across clients. Using these synthetic data as a training set, we alleviate the local overfitting problem by adaptively generalizing the absent knowledge within each local dataset. This enhances the generalization capabilities of local models, enabling them to handle unseen data effectively. Our model outperforms baselines in our proposed experimental settings, which are designed to measure generalization power to unseen data in practical scenarios. Our code is available at https://github.com/sung-won-kim/FedLoG
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