Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features among multiple parties. Most VFL algorithms primarily rely on two fundamental privacy-preserving techniques: Homomorphic Encryption (HE) and secure Multi-Party Computation (MPC). Though generally considered with stronger privacy guarantees, existing general-purpose MPC frameworks suffer from expensive computation and communication overhead and are inefficient especially under VFL settings. This study centers around MPC-based VFL algorithms and presents a novel approach for two-party vertical federated linear models via an efficient secret sharing (SS) scheme with a trusted coordinator. Our approach can achieve significant acceleration of the training procedure in vertical federated linear models of between 2.5x and 6.6x than other existing MPC frameworks under the same security setting.

翻译：纵向联邦学习（VFL）已成为安全协作机器学习中最主流的方法之一，其训练数据按特征维度分布在多方之间。大多数VFL算法主要依赖两种基础隐私保护技术：同态加密（HE）和安全多方计算（MPC）。尽管现有通用MPC框架通常被认为具有更强的隐私保障，但其存在高昂的计算与通信开销，尤其在VFL场景下效率低下。本研究聚焦于基于MPC的VFL算法，通过引入带可信协调器的高效秘密共享（SS）方案，提出了一种用于两方纵向联邦线性模型的新方法。在相同安全设置下，本方法相比现有其他MPC框架，可将纵向联邦线性模型的训练过程加速2.5倍至6.6倍。