论文标题
劫持垂直联合学习模型作为一个聚会
Hijack Vertical Federated Learning Models As One Party
论文作者
论文摘要
垂直联合学习（VFL）是一个新兴的范式，使合作者能够以分布式方式一起构建机器学习模型。通常，这些政党有一组共同的用户，但拥有不同的功能。现有的VFL框架使用加密技术来提供数据隐私和安全保证，从而导致研究计算效率和快速实施的一系列工作。但是，VFL模型的安全性仍未得到充实。
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.
