Vertically Federated Learning with Correlated Differential Privacy

Abstract

Federated learning (FL) aims to address the challenges of data silos and privacy protection in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces and overlapping ID spaces can capture more knowledge and facilitate model learning. However, VFL has both privacy and utility problems in framework construction. On the one hand, sharing gradients may cause privacy leakage. On the other hand, the increase in participants brings a surge in the feature dimension of the global model, which results in higher computation costs and lower model accuracy. To address these issues, we propose a vertically federated learning algorithm with correlated differential privacy (CRDP-FL) to meet FL systems’ privacy and utility requirements. A privacy-preserved VFL framework is designed based on differential privacy (DP) between organizations with many network edge devices. Meanwhile, feature selection is performed to improve the algorithm’s efficiency and model performance to solve the problem of dimensionality explosion. We also propose a quantitative correlation analysis technique for VFL to reduce the correlated sensitivity and noise injection, balancing the utility decline due to DP protection. We theoretically analyze the privacy level and utility of CRDP-FL. A real vertically federated learning scenario is simulated with personalized settings based on the ISOLET and Breast Cancer datasets to verify the method’s effectiveness in model accuracy, privacy budget, and data correlation.