剖析真实场景下垂直联邦学习的落地难题，提出新分类框架
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
- 基于真实数据分布构建新型垂直联邦学习算法分类体系
- 发现多数实际场景缺乏可行解决方案，研究与应用存在明显脱节
- 为跨组织协作中的隐私计算提供可落地的研究方向
垂直联邦学习（VFL）是一种保护隐私的协同学习范式，允许多个拥有不同特征集的机构在不共享原始数据的前提下联合训练机器学习模型。尽管其有望推动跨组织合作，但当前实际部署仍十分有限。为探究现有研究与实际应用之间的差距，本文分析了潜在VFL应用场景中的真实数据分布，揭示四个关键发现。我们基于真实数据分布提出了一个全新的数据导向型VFL算法分类体系。对现有VFL算法的综合回顾表明，一些常见实际场景中缺乏有效解决方案。基于这些观察，本文明确了若干关键研究方向，旨在缩小当前VFL研究与真实应用之间的鸿沟。
原文摘要 · Abstract (English)
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL systems in real-world applications remains limited. To investigate the gap between existing VFL research and practical deployment, this survey analyzes the real-world data distributions in potential VFL applications and identifies four key findings that highlight this gap. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Our comprehensive review of existing VFL algorithms reveals that some common practical VFL scenarios have few or no viable solutions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.
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