High value passenger identification research based on Federated Learning

Nowadays, airlines are facing increasingly fierce market competition while ushering in development opportunities. Many scholars researched on airline passenger value using data mining approaches, but the evaluation index of air passenger value in the existing research is based on internal data sources. It is of great importance to blend the external data from third-party under the premise of safe and legal data privacy disclosure to extend the characteristic dimension of their customers. Therefore, this research proposes a novel model that can blend multi-source big data to enrich airline passengers' feature dimensions under the premise of ensuring passengers' information privacy security, and establish the user profile of passengers for accurately identifying the high-value passengers. It is proved that our proposed novel model has better performance compared with the results of the traditional model that only use one party data in terms of Area Under Curve (AUC) and Kolmogorov-Smirnov (KS) value.