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Vertical Federated Learning (VFL) is a crucial paradigm for training machinelearning models on feature-partitioned, distributed data. However, due toprivacy restrictions, few public real-world VFL datasets exist for algorithmevaluation, and these represent a limited array of feature distributions.Existing benchmarks often resort to synthetic datasets, derived from arbitraryfeature splits from a global set, which only capture a subset of featuredistributions, leading to inadequate algorithm performance assessment. Thispaper addresses these shortcomings by introducing two key factors affecting VFLperformance - feature importance and feature correlation - and proposingassociated evaluation metrics and dataset splitting methods. Additionally, weintroduce a real VFL dataset to address the deficit in image-image VFLscenarios. Our comprehensive evaluation of cutting-edge VFL algorithms providesvaluable insights for future research in the field.

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks