  The incredible development of federated learning (FL) has benefited various
tasks in the domains of computer vision and natural language processing, and
the existing frameworks such as TFF and FATE has made the deployment easy in
real-world applications. However, federated graph learning (FGL), even though
graph data are prevalent, has not been well supported due to its unique
characteristics and requirements. The lack of FGL-related framework increases
the efforts for accomplishing reproducible research and deploying in real-world
applications. Motivated by such strong demand, in this paper, we first discuss
the challenges in creating an easy-to-use FGL package and accordingly present
our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified
view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo
and ModelZoo for out-of-the-box FGL capability; (3) an efficient model
auto-tuning component; and (4) off-the-shelf privacy attack and defense
abilities. We validate the effectiveness of FS-G by conducting extensive
experiments, which simultaneously gains many valuable insights about FGL for
the community. Moreover, we employ FS-G to serve the FGL application in
real-world E-commerce scenarios, where the attained improvements indicate great
potential business benefits. We publicly release FS-G, as submodules of
FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's
research and enable broad applications that would otherwise be infeasible due
to the lack of a dedicated package.
