Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.

翻译：纵向联邦学习（VFL）是一种新兴范式，使协作者能够以分布式方式共同构建机器学习模型。通常情况下，这些参与方拥有一组共同用户，但各自持有不同的特征。现有VFL框架采用密码学技术来提供数据隐私和安全性保障，催生了一系列关于计算效率与快速实现的研究。然而，VFL模型的安全性仍尚未得到充分探索。