Q-VFL: quantum-enhanced vertical federated learning with contrastive encoding for privacy-preserving medical AI

Abstract

Medical institutions face significant challenges in collaborative machine learning due to diverse feature distributions, strict privacy regulations, and the critical issue of data scarcity. Traditional federated learning approaches often struggle with poor generalization and inadequate privacy protection in these sensitive healthcare settings. This paper presents a Quantum-Enhanced Vertical Federated Learning (Q-VFL) framework that effectively combines quantum feature encoding with supervised contrastive learning. Our approach employs a three-stage quantum circuit architecture for robust feature encoding, followed by classical neural networks, implemented through a privacy-preserving split learning procedure. Experimental results across diverse medical imaging datasets demonstrate that the proposed Q-VFL framework achieves strong performance, with accuracy gains of up to 25 percentage points over quantum cross-entropy baselines and competitive results against classical methods. Beyond raw performance, the framework exhibits high clinical reliability and stability. Crucially, it provides stronger privacy guarantees: detailed analysis shows that Q-VFL reduces membership inference attack success rates by 10–15% relative to deep classical VFL baselines, with avg. 0.59 vs. 0.68. Q-VFL also achieves near-zero model inversion success on MedMNIST datasets (0.001). These findings identify Q-VFL as a promising solution for secure and collaborative medical AI.