Practical Privacy Attacks on Vertical Federated Learning

Understand

Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private data.

• According to the form of collaboration, FL can be further divided into horizontal federated learning (HFL) and vertical federated learning (VFL).
• In HFL, participants share the same feature space and collaborate on data samples, while in VFL, participants share the same sample IDs and collaborate on features.
• VFL has a broader scope of applications and is arguably more suitable for joint model training between large enterprises.