  With the development of network technology, many social media are
flourishing. Due to imperfect Internet regulation, the spread of false rumors
has become a common problem on those social platforms. Social platforms can
generate rumor data in their operation process, but existing rumor detection
models are all constructed for a single social platform, which ignores the
value of cross-platform rumor. This paper combines the federated learning
paradigm with the bidirectional graph attention network rumor detection model
and proposes the federated graph attention network(FedGAT) model for rumor
detection. Taking the advantages of horizontal federated learning for
cross-platform rumor detection, the security of each social platform's data
information can be ensured. We construct both the server-side and client-side
models as a bidirectional graph attention network rumor detection model in the
federated learning paradigm framework. The local model on the client-side can
train and verify the rumor data of the social platform in the iterative
process, while the model on the server-side only plays the role of aggregating
the characteristics of different social platform models and does not
participate in the training of the model. Finally, we conduct simulation
experiments on the rumor datasets and the experimental results show that the
federated graph attention network model proposed in this paper is effective for
cross-platform rumor detection.
