  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.
