Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization

TL;DR

This paper introduces a cascaded hybrid optimization approach for vertical federated learning that significantly accelerates convergence while preserving privacy, enabling practical training of large models.

Contribution

The paper proposes a novel cascaded hybrid optimization method combining zeroth-order and first-order updates to improve convergence speed in VFL without sacrificing privacy.

Findings

Faster convergence than ZOO-based VFL.

Maintains privacy comparable to existing methods.

Enables training of large models in VFL.

Abstract

Vertical Federated Learning (VFL) attracts increasing attention because it empowers multiple parties to jointly train a privacy-preserving model over vertically partitioned data. Recent research has shown that applying zeroth-order optimization (ZOO) has many advantages in building a practical VFL algorithm. However, a vital problem with the ZOO-based VFL is its slow convergence rate, which limits its application in handling modern large models. To address this problem, we propose a cascaded hybrid optimization method in VFL. In this method, the downstream models (clients) are trained with ZOO to protect privacy and ensure that no internal information is shared. Meanwhile, the upstream model (server) is updated with first-order optimization (FOO) locally, which significantly improves the convergence rate, making it feasible to train the large models without compromising privacy and security. We theoretically prove that our VFL framework converges faster than the ZOO-based VFL, as the convergence of our framework is not limited by the size of the server model, making it effective for training large models with the major part on the server. Extensive experiments demonstrate that our method achieves faster convergence than the ZOO-based VFL framework, while maintaining an equivalent level of privacy protection. Moreover, we show that the convergence of our VFL is comparable to the unsafe FOO-based VFL baseline. Additionally, we demonstrate that our method makes the training of a large model feasible.