FedPV-FS: A Feature Selection Method for Federated Learning in Insurance Precision Marketing

Insurance companies always use federated learning to integrate external data sources for data analysis and improve the conversion rate of insurance precision marketing. However, due to imbalanced data distribution and the presence of null data, the joint modeling often suffers from low robustness and is prone to falling into the dilemma of under-fitting. Therefore, the feature selection for federated learning needs to be incorporated before the joint modeling to improve the accuracy of predictions. In this paper, we propose the FedPV-FS method, which includes two-party feature selection based on public verifiable covert (PVC), and multi-party federated feature selection based on verifiable secret sharing (VSS). Moreover, we iteratively optimize federated feature selection using data selection, transformation, and integration. Experiments show that our method can achieve high-quality feature selection for increasing the optimization objective to 88.4%, promote the continuous increase of insurance premiums, and has good applications in insurance precision marketing scenarios.