Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to protect the privacy of their individual data. Notably, in VFL, the labels are available to a single party and the complete feature set is formed only when data from all parties is combined. Recently, Xu et al. proposed a new framework called FedV for secure gradient computation for VFL using multi-input functional encryption. In this work, we explain how some of the information leakage in Xu et al. can be avoided by using Quadratic functional encryption when training generalized linear models for vertical federated learning.

翻译：纵向联邦学习（VFL）能够在多方数据分布的设置下协同训练机器学习（ML）模型，同时保护各方的个体数据隐私。值得注意的是，在VFL中，标签仅由单一参与方掌握，而完整的特征集只有在所有参与方数据合并后才能形成。近期，Xu等提出了一种名为FedV的新框架，利用多输入函数加密实现VFL的安全梯度计算。本研究阐释了如何通过在纵向联邦学习训练广义线性模型时采用二次函数加密，避免Xu等人方案中部分信息泄露的问题。