  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.
