Efficient Vertical Federated Learning with Secure Aggregation

TL;DR

This paper introduces a secure and efficient method for vertical federated learning that maintains training performance while significantly reducing computational overhead compared to homomorphic encryption.

Contribution

The authors propose a novel secure aggregation approach for vertical federated learning that improves efficiency without sacrificing privacy or accuracy.

Findings

Achieves 9.1e2 to 3.8e4 times speedup over homomorphic encryption

Maintains training performance comparable to non-secure methods

Provides a practical solution for privacy-preserving vertical FL

Abstract

The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However, in many interesting problems, such as financial fraud detection and disease detection, individual data points are scattered across different clients/organizations in vertical federated learning. Solutions for this type of FL require the exchange of gradients between participants and rarely consider privacy and security concerns, posing a potential risk of privacy leakage. In this work, we present a novel design for training vertical FL securely and efficiently using state-of-the-art security modules for secure aggregation. We demonstrate empirically that our method does not impact training performance whilst obtaining 9.1e2 ~3.8e4 speedup compared to homomorphic encryption (HE).