Semi-VFL: Communication-efficient Few-label Vertical Federated Learning with Stacked Generalization and Model-level Consistency

Abstract

Vertical federated learning (VFL) is a collaborative learning scheme where clients share some overlapping samples but have different feature spaces. Existing VFL schemes are restricted in model performance and deployment feasibility due to the scarcity of overlapping labeled samples and high communication costs. To tackle these issues, we propose a practical VFL scheme Semi-VFL using stacked generalization, which can effectively improve model performance with limited aligned labeled samples and only two communication times. A built-in local semi-supervised learning strategy FewMatch with model-level consistency for few-label VFL setting is designed in our scheme. Extensive experiments indicate the superiority of Semi-VFL in both image and tabular datasets. Specifically, Semi-VFL can achieve accuracy improvement by more than 10.9% and communication cost reduction by more than 380× over the state-of-the-art few-shot VFL scheme on CIFAR-10 with 128 aligned samples.