GAN-based Vertical Federated Learning for Label Protection in Binary Classification

Understand

Split learning (splitNN) has emerged as a popular strategy for addressing the high computational costs and low modeling efficiency in Vertical Federated Learning (VFL).

• However, despite its popularity, vanilla splitNN lacks encryption protection, leaving it vulnerable to privacy leakage issues, especially Label Leakage from Gradients (LLG).
• Motivated by the LLG issue resulting from the use of labels during training, we propose the Generative Adversarial Federated Model (GAFM), a novel method designed specifically to enhance label privacy protection by integrating splitNN with Generative Adversarial Networks (GANs).
• GAFM leverages GANs to indirectly utilize label information by learning the label distribution rather than relying on explicit labels, thereby mitigating LLG.