VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

arXiv:2307.02040v3 Announce Type: replace
Abstract: Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmarks often resort to synthetic datasets, derived from arbitrary feature splits from a global set, which only capture a subset of feature distributions, leading to inadequate algorithm performance assessment. This paper addresses these shortcomings …

abstract algorithm array arxiv benchmarks cs.ai cs.lg data datasets distributed distributed data distribution diversity evaluation feature federated learning however machine machine learning machine learning models paradigm privacy public restrictions training type world