  Graph Neural Networks (GNNs) have been widely used for various types of graph
data processing and analytical tasks in different domains. Training GNNs over
centralized graph data can be infeasible due to privacy concerns and regulatory
restrictions. Thus, federated learning (FL) becomes a trending solution to
address this challenge in a distributed learning paradigm. However, as GNNs may
inherit historical bias from training data and lead to discriminatory
predictions, the bias of local models can be easily propagated to the global
model in distributed settings. This poses a new challenge in mitigating bias in
federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair
Federated Graph Neural Network, that enhances group fairness of federated GNNs.
As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN
aims to mitigate both types of bias under federated settings. First, we provide
theoretical insights on the connection between data bias in a training graph
and statistical fairness metrics of the trained GNN models. Based on the
theoretical analysis, we design $\text{F}^2$GNN which contains two key
components: a fairness-aware local model update scheme that enhances group
fairness of the local models on the client side, and a fairness-weighted global
model update scheme that takes both data bias and fairness metrics of local
models into consideration in the aggregation process. We evaluate
$\text{F}^2$GNN empirically versus a number of baseline methods, and
demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both
fairness and model accuracy.
