MI-VFDNN: An Efficient Vertical Federated Deep Neural Network With Multi-Layer Interaction

Federated Learning (FL) is proposed to address the challenge of data isolation, with federated machine learning algorithms continuously evolving. Vertical Federated Learning (VFL) is a specific FL setting where parties possessing different features but the same entities collaboratively train models. However, existing VFL neural network schemes lack sufficient interactivity between involved parties, which results in diminished data value as model complexity increases. This limitation can lead to reduced accuracy with more intricate model structures. In our proposed scheme, we introduce additional interactions to enhance the effective utilization of data owned by the parties during the training period. To maintain security, we design a novel protocol that utilizes dimensionality reduction methods, ensuring interactions occur without information leakage and excessive communication costs. Experimental comparisons among various schemes validate the algorithm’s efficiency considerably. Additionally, we assess the model’s robustness against data poisoning attacks.