Subgraphs of a larger global graph may be distributed across multiple devices, and only locally accessible due to privacy restrictions, although there may be links between subgraphs. Recently proposed subgraph Federated Learning (FL) methods deal with those missing links across local subgraphs while distributively training Graph Neural Networks (GNNs) on them. However, they have overlooked the inevitable heterogeneity between subgraphs comprising different communities of a global graph, consequently collapsing the incompatible knowledge from local GNN models. To this end, we introduce a new subgraph FL problem, personalized subgraph FL, which focuses on the joint improvement of the interrelated local GNNs rather than learning a single global model, and propose a novel framework, FEDerated Personalized sUBgraph learning (FED-PUB), to tackle it. Since the server cannot access the subgraph in each client, FED-PUB utilizes functional embeddings of the local GNNs using random graphs as inputs to compute similarities between them, and use the similarities to perform weighted averaging for server-side aggregation. Further, it learns a personalized sparse mask at each client to select and update only the subgraph-relevant subset of the aggregated parameters. We validate our FED-PUB for its subgraph FL performance on six datasets, considering both non-overlapping and overlapping subgraphs, on which it significantly outperforms relevant baselines. Our code is available at https://github.com/JinheonBaek/FED-PUB.

翻译：更大全局图的子图可能分布在多个设备上，且由于隐私限制仅能在本地访问，尽管子图之间可能存在链接。近期提出的子图联邦学习方法在处理本地子图间缺失链接的同时，分布式地训练图神经网络。然而，它们忽视了由全局图不同社区构成的子图之间不可避免的异质性，从而导致本地图神经网络的不兼容知识崩溃。为此，我们引入了一个新的子图联邦学习问题——个性化子图联邦学习，其重点在于共同改进相互关联的本地图神经网络，而非学习单一全局模型，并提出了一种新型框架FEDerated Personalized sUBgraph learning (FED-PUB) 来解决该问题。由于服务器无法访问每个客户端的子图，FED-PUB利用以随机图作为输入的本地图神经网络的功能嵌入来计算它们之间的相似性，并利用这些相似性对服务器端聚合执行加权平均。此外，它在每个客户端学习一个个性化稀疏掩码，仅选择和更新聚合参数中的子图相关子集。我们在六个数据集上验证了FED-PUB的子图联邦学习性能，考虑了非重叠和重叠子图场景，其性能显著优于相关基线方法。我们的代码可在 https://github.com/JinheonBaek/FED-PUB 获取。