  Subgraph federated learning (SFL) is a research methodology that has gained
significant attention for its potential to handle distributed graph-structured
data. In SFL, the local model comprises graph neural networks (GNNs) with a
partial graph structure. However, some SFL models have overlooked the
significance of missing cross-subgraph edges, which can lead to local GNNs
being unable to message-pass global representations to other parties' GNNs.
Moreover, existing SFL models require substantial labeled data, which limits
their practical applications. To overcome these limitations, we present a novel
SFL framework called FedMpa that aims to learn cross-subgraph node
representations. FedMpa first trains a multilayer perceptron (MLP) model using
a small amount of data and then propagates the federated feature to the local
structures. To further improve the embedding representation of nodes with local
subgraphs, we introduce the FedMpae method, which reconstructs the local graph
structure with an innovation view that applies pooling operation to form
super-nodes. Our extensive experiments on six graph datasets demonstrate that
FedMpa is highly effective in node classification. Furthermore, our ablation
experiments verify the effectiveness of FedMpa.
