A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning. (arXiv:2209.03885v1 [cs.LG])

Federated learning (FL) has emerged as a practical solution to tackle data
silo issues without compromising user privacy. One of its variants, vertical
federated learning (VFL), has recently gained increasing attention as the VFL
matches the enterprises' demands of leveraging more valuable features to build
better machine learning models while preserving user privacy. Current works in
VFL concentrate on developing a specific protection or attack mechanism for a
particular VFL algorithm. In this work, we propose an evaluation framework that …

federated learning framework privacy trade utility