  Graph-based semi-supervised node classification (GraphSSC) has wide
applications, ranging from networking and security to data mining and machine
learning, etc. However, existing centralized GraphSSC methods are impractical
to solve many real-world graph-based problems, as collecting the entire graph
and labeling a reasonable number of labels is time-consuming and costly, and
data privacy may be also violated. Federated learning (FL) is an emerging
learning paradigm that enables collaborative learning among multiple clients,
which can mitigate the issue of label scarcity and protect data privacy as
well. Therefore, performing GraphSSC under the FL setting is a promising
solution to solve real-world graph-based problems. However, existing FL methods
1) perform poorly when data across clients are non-IID, 2) cannot handle data
with new label domains, and 3) cannot leverage unlabeled data, while all these
issues naturally happen in real-world graph-based problems. To address the
above issues, we propose the first FL framework, namely GraphFL, for
semi-supervised node classification on graphs. Our framework is motivated by
meta-learning methods. Specifically, we propose two GraphFL methods to
respectively address the non-IID issue in graph data and handle the tasks with
new label domains. Furthermore, we design a self-training method to leverage
unlabeled graph data. We adopt representative graph neural networks as GraphSSC
methods and evaluate GraphFL on multiple graph datasets. Experimental results
demonstrate that GraphFL significantly outperforms the compared FL baseline and
GraphFL with self-training can obtain better performance.
