Social networks are ubiquitous in contemporary society, where one's opinions and behaviors are easily influenced by those around them. The effectiveness of social influence analysis has become significant for numerous application fields such as online recommendation, advertising, and viral marketing. Graph-based deep learning has achieved significant success in influence prediction. However, the exist-ing methods ignore the privacy issue and are incapable of cross-organizational collaboration. Inspired by the recent success of federated learning in privacy protection and breaking the data barrier. In this paper, a novel federated learning framework, FedInf, is proposed to tackle the problem of social influ-ence prediction. Specially, to further protect user privacy and achieve secure aggregation for multiple local models, we introduce differential privacy into the local parameters, adding artificial noise before model aggregation. In order to trade-off utility and privacy, we first freeze the embedding layers to reduce the number of upload parameters, and then project model parameters into low-dimensional space. Therefore, less noise is required to provide the same level of privacy protection. Extensive exper-iments on four real-world datasets demonstrate that the proposed FedInf is effective while providing privacy protection.& COPY; 2023 Elsevier B.V. All rights reserved.
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