  As special information carriers containing both structure and feature
information, graphs are widely used in graph mining, e.g., Graph Neural
Networks (GNNs). However, in some practical scenarios, graph data are stored
separately in multiple distributed parties, which may not be directly shared
due to conflicts of interest. Hence, federated graph neural networks are
proposed to address such data silo problems while preserving the privacy of
each party (or client). Nevertheless, different graph data distributions among
various parties, which is known as the statistical heterogeneity, may degrade
the performance of naive federated learning algorithms like FedAvg. In this
paper, we propose FedEgo, a federated graph learning framework based on
ego-graphs to tackle the challenges above, where each client will train their
local models while also contributing to the training of a global model. FedEgo
applies GraphSAGE over ego-graphs to make full use of the structure information
and utilizes Mixup for privacy concerns. To deal with the statistical
heterogeneity, we integrate personalization into learning and propose an
adaptive mixing coefficient strategy that enables clients to achieve their
optimal personalization. Extensive experimental results and in-depth analysis
demonstrate the effectiveness of FedEgo.
