FDRP: federated deep relationship prediction with sequential information.

Social relationship prediction has garnered significant attention in the development of artificial intelligence technology due to its potential to promote socioeconomic development. Meantime, the increasing popularity and adoption of intelligent devices located in the Internet of Things (IoT) environment have generated abundant data, which can serve as a basis for social relationship prediction. Nevertheless, the highly fragmented distribution of user data and the implementation of data protection policies can lead to the dilemma of "data scarcity" during the process of social relationship prediction. The application of federated learning can effectively address data segregation and fragmentation. Additionally, existing studies on social relationship prediction fail to consider users' temporal sequence information. To this end, we present a novel Federated Deep Relationship Prediction framework, named FDRP, which adopts the principle of vertical federated learning. Specifically, under the IoT environment, the offline operation initially assigns virtual items and ratings to users. Subsequently, the online operation executes social relationship prediction, where the client converts the sparse data into dense vectors and extracts the overall temporal sequence features, as well as the server performs the model parameter aggregation. Through extensive experiments conducted on the Epinions dataset, FDRP demonstrates excellent prediction accuracy and convergence speed, effectively mitigating the risk of inference attacks. [ABSTRACT FROM AUTHOR]