Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues. For the first issue, the server model of M$^3$FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M$^3$FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.
翻译:研究人员将联邦学习(FL)和图模型相结合,以解决时空预测的挑战,并考虑了隐私和安全的限制。为了更好地利用图模型的功能,一些研究人员还结合了分裂学习(SL)。然而,仍有几个问题未得到解决:1)在推断阶段,客户端可能无法访问服务器;2)服务器模型中手动设计的客户端图可能无法显示客户端之间的正确关系。本文提出了一种新的面向GNN的分裂联邦学习方法,命名为节点遮蔽和多尺度消息传递的联邦图模型(M 3 FGM),用于解决上述问题。针对第一个问题,M 3 FGM 的服务器模型采用 MaskNode 层来模拟处于离线状态的客户端情况。我们还使用双子解码器结构重新设计了客户端模型的解码器,以便每个客户端模型在离线时可以使用其本地数据进行独立预测。针对第二个问题,一种名为多尺度消息传递(MGMP)层的新 GNN 层使每个客户端节点能够感知全局和本地信息。我们在两个真实交通数据集的两个不同场景下进行了广泛的实验。结果显示,M 3 FGM 胜过基线和变体模型,在两个数据集和场景中均取得了最佳结果。