Differentially Private Vertical Federated Learning

Understand

A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models.

• Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applications, however with large costs and human efforts to label training data.
• Recent advancements in federated learning (FL) allow multiple data owners or organisations to collaboratively train a machine learning model without sharing raw data.
• In this light, vertical FL allows organisations to build a global model when the participating organisations have vertically partitioned data.