  Collaborative graph analysis across multiple institutions is becoming
increasingly popular. Realistic examples include social network analysis across
various social platforms, financial transaction analysis across multiple banks,
and analyzing the transmission of infectious diseases across multiple
hospitals. We define the federated graph analytics, a new problem for
collaborative graph analytics under differential privacy. Although
differentially private graph analysis has been widely studied, it fails to
achieve a good tradeoff between utility and privacy in federated scenarios, due
to the limited view of local clients and overlapping information across
multiple subgraphs. Motivated by this, we first propose a federated graph
analytic framework, named FEAT, which enables arbitrary downstream common graph
statistics while preserving individual privacy. Furthermore, we introduce an
optimized framework based on our proposed degree-based partition algorithm,
called FEAT+, which improves the overall utility by leveraging the true local
subgraphs. Finally, extensive experiments demonstrate that our FEAT and FEAT+
significantly outperform the baseline approach by approximately one and four
orders of magnitude, respectively.
