A Nearest Neighbor Under-sampling Strategy for Vertical Federated Learning in Financial Domain

Machine learning techniques have been widely applied in modern financial activities. Participants in the field are aware of the importance of data privacy. Vertical federated learning (VFL) was proposed as a solution to multi-party secure computation for machine learning to obtain the huge data required by the models as well as keep the privacy of the data holders. However, previous research majorly analyzed the algorithms under ideal conditions. Data imbalance in VFL is still an open problem. In this paper, we propose a privacy-preserving sampling strategy for imbalanced VFL based on federated graph embedding of the samples, without leaking any distribution information. The participants of the federation provide partial neighbor information for each sample during the intersection stage and the controversial negative sample will be filtered out. Experiments were conducted on commonly used financial datasets and one real-world dataset. Our proposed approach obtained the leading F1 score on all tested datasets on comparing with the baseline under sampling strategies for VFL.