提出去中心化垂直联邦学习新方案，提升隐私保护下的模型效率。
De-VertiFL: A Solution for Decentralized Vertical Federated Learning
- 通过共享隐藏层输出实现客户端间知识协作
- 在多类分类任务中F1分数优于现有方法
- 适合数据敏感且分布异构的跨机构场景
联邦学习（FL）自2016年提出以来，旨在提升协同建模中的数据隐私。水平联邦学习（客户端拥有相同特征但不同样本）已在集中式与去中心化环境中广泛研究，而垂直联邦学习（客户端对同一实体拥有不同但敏感的数据）在真实去中心化场景中至关重要，却仍研究不足。本文提出De-VertiFL，一种面向去中心化垂直联邦学习的新方案，引入新型网络结构分配、创新的知识交换机制与分布式训练流程。De-VertiFL支持客户端间共享隐藏层输出，使各方受益于中间计算结果，从而提升学习效率。该方法在多种知名数据集上进行评估，涵盖图像与表格数据，涉及二分类与多分类任务。实验表明，De-VertiFL在保持去中心化与隐私保护的前提下，整体性能优于当前先进方法，尤其在F1-score指标上表现突出。
原文摘要 · Abstract (English)
Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings. In contrast, Vertical Federated Learning (VFL), which is crucial in real-world decentralized scenarios where clients possess different, yet sensitive, data about the same entity, remains underexplored. Thus, this work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting. De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process. Specifically, De-VertiFL enables the sharing of hidden layer outputs among federation clients, allowing participants to benefit from intermediate computations, thereby improving learning efficiency. De-VertiFL has been evaluated using a variety of well-known datasets, including both image and tabular data, across binary and multiclass classification tasks. The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.
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