论文标题
M3FGM：一个节点掩蔽和多粒性消息传递基于时空数据预测的联合图模型
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction
论文作者
论文摘要
研究人员正在通过将联合学习（FL）和图形模型相结合的隐私和安全性来解决空间周期预测的挑战。为了更好地利用图形模型的力量，一些研究还结合了分裂学习（SL）。但是，仍然有几个问题无人看管：1）客户在推理阶段可能无法访问服务器； 2）在服务器模型中手动设计的客户端图可能无法揭示客户端之间的正确关系。本文提出了一种新的面向GNN的分裂联合学习方法，名为Node {\ bfseries m}和{\ bfseries m} ulti-granaularity {\ bfseries m}基于基于基于的联合图形模型（M $^3 $ fgm），以上问题。对于第一期，M $^3 $ fgm的服务器模型采用MaskNode层来模拟客户离线的情况。我们还使用双次数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独立预测其本地数据。至于第二期，名为多粒度消息传递（MGMP）层的新的GNN层使每个客户端节点都能感知全局和本地信息。我们在两个真实流量数据集的两个不同方案中进行了广泛的实验。结果表明，M $^3 $ fgm的表现优于基线和变体模型，在数据集和方案中都取得了最佳结果。
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.
