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）能够在数据分布在多个参与者之间且各参与者希望保护其个体数据隐私的场景中，支持机器学习（ML）模型的协同训练。值得注意的是，在VFL中，标签仅由单一参与者持有，而完整的特征集仅当所有参与方的数据合并后方才形成。近期，Xu等人提出了一种名为FedV的新框架，用于基于多输入功能加密的VFL安全梯度计算。本研究阐述了在针对纵向联邦学习训练广义线性模型时，如何通过使用二次功能加密避免Xu等人工作中存在的部分信息泄露问题。