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With the development of network technology, many social media areflourishing. Due to imperfect Internet regulation, the spread of false rumorshas become a common problem on those social platforms. Social platforms cangenerate rumor data in their operation process, but existing rumor detectionmodels are all constructed for a single social platform, which ignores thevalue of cross-platform rumor. This paper combines the federated learningparadigm with the bidirectional graph attention network rumor detection modeland proposes the federated graph attention network(FedGAT) model for rumordetection. Taking the advantages of horizontal federated learning forcross-platform rumor detection, the security of each social platform's datainformation can be ensured. We construct both the server-side and client-sidemodels as a bidirectional graph attention network rumor detection model in thefederated learning paradigm framework. The local model on the client-side cantrain and verify the rumor data of the social platform in the iterativeprocess, while the model on the server-side only plays the role of aggregatingthe characteristics of different social platform models and does notparticipate in the training of the model. Finally, we conduct simulationexperiments on the rumor datasets and the experimental results show that thefederated graph attention network model proposed in this paper is effective forcross-platform rumor detection.

Federated Graph Attention Network for Rumor Detection