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：摘要：研究者们正通过结合联邦学习与图模型，在隐私和安全约束下解决时空预测的挑战。为更好地发挥图模型的能力，部分研究还引入了分割学习。然而，仍存在若干未解决的问题：1）推理阶段客户端可能无法访问服务器；2）服务器模型中人工设计的客户端图可能无法揭示客户端间的真实关联。本文针对上述问题提出一种新的面向图神经网络的分割联邦学习方法，即基于节点掩码与多粒度消息传递的联邦图模型（M³FGM）。针对第一个问题，M³FGM的服务端模型采用MaskNode层来模拟客户端离线场景，同时利用双子解码器结构重新设计客户端模型的解码器，使得每个客户端模型在离线时可独立利用本地数据进行预测。针对第二个问题，本文提出一种名为多粒度消息传递（MGMP）的新型图神经网络层，使每个客户端节点能够感知全局与局部信息。我们在两个真实交通数据集上针对两种不同场景开展了大量实验。结果表明，M³FGM在性能上优于基线模型与变体模型，在两个数据集和场景中均取得了最佳结果。