论文标题
多参与者多级垂直联合学习
Multi-Participant Multi-Class Vertical Federated Learning
论文作者
论文摘要
Federated Learning（FL）是一种保护集体机器学习模型的隐私范式，该模型具有来自多个参与者的本地存储数据。垂直联合学习（VFL）涉及参与者共享相同样本ID空间但具有不同特征空间的情况，而标签信息则由一个参与者拥有。当前对VFL的研究仅支持两名参与者，主要关注二进制型逻辑回归问题。在本文中，我们提出了多个涉及多个政党的多级VFL问题的多参与者多级垂直联合学习（MMVFL）框架。 MMVFL扩展了多视图学习的想法（MVL），可以以隐私的方式将其从其所有者分享到其他VFL参与者。为了证明MMVFL的有效性，将功能选择方案合并到MMVFL中，以将其性能与监督功能选择和基于MVL的方法进行比较。实际数据集的实验结果表明，MMVFL可以有效地在多个VFL参与者之间共享标签信息，并匹配现有方法的多类分类性能。
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL problems involving multiple parties. Extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacypreserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. Experiment results on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match multi-class classification performance of existing approaches.
