{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vertibench-advancing-feature-distribution","title":"VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks","arxiv_id":"2307.02040","date":"2023-07-05","proceeding":null,"authors":["Zhaomin Wu","Junyi Hou","Bingsheng He"],"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 by introducing two key factors affecting VFL performance - feature importance and feature correlation - and proposing associated evaluation metrics and dataset splitting methods. Additionally, we introduce a real VFL dataset to address the deficit in image-image VFL scenarios. Our comprehensive evaluation of cutting-edge VFL algorithms provides valuable insights for future research in the field.","url_abs":"https://arxiv.org/abs/2307.02040v3","url_pdf":"https://arxiv.org/pdf/2307.02040v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vertibench-advancing-feature-distribution","repo_url":"https://github.com/Xtra-Computing/VertiBench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"feature-correlation","task_name":"Feature Correlation"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"vertical-federated-learning","task_name":"Vertical Federated Learning"}],"methods":[],"datasets_introduced":[{"slug":"satellite","name":"Satellite","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}