Vertical federated learning-based feature selection with non-overlapping sample utilization

Siwei Feng

Abstract: Highlights•In this paper, we bridge this gap by proposing a novel VFL-based feature selection method—Vertical Federated Learning-based Feature Selection (VFLFS). To the best of our knowledge, this is the first deep learning-based vertical federated learning approach with feature selection.•A strategy to make use of non-overlapping samples is also proposed to improve feature selection effectiveness.•The proposed VFLFS approach has been evaluated extensively based on real-world datasets. The results show that VFLFS can significantly improve model performance under VFL settings compared to four state of the art baselines, especially in conditions where a large proportion of the data samples do not overlap across data owners.