Vertical Federated Learning (VFL) is an emergent distributed machine learning paradigm wherein owners of disjoint features of a common set of entities collaborate to learn a global model without sharing data. In VFL, a host client owns data labels for each entity and learns a final representation based on intermediate local representations from all guest clients. Therefore, the host is a single point of failure and label feedback can be used by malicious guest clients to infer private features. Requiring all participants to remain active and trustworthy throughout the entire training process is generally impractical and altogether infeasible outside of controlled environments. We propose Decoupled VFL (DVFL), a blockwise learning approach to VFL. By training each model on its own objective, DVFL allows for decentralized aggregation and isolation between feature learning and label supervision. With these properties, DVFL is fault tolerant and secure. We implement DVFL to train split neural networks and show that model performance is comparable to VFL on a variety of classification datasets.

翻译：纵向联邦学习（VFL）是一种新兴的分布式机器学习范式，其允许持有同一实体集合不同特征的数据所有者在不共享数据的情况下协作训练全局模型。在VFL中，主机客户端拥有每个实体的数据标签，并基于所有访客客户端的中间局部表示学习最终表征。因此，主机成为单点故障源，且恶意访客客户端可能利用标签反馈推断私有特征。要求所有参与方在整个训练过程中始终保持活跃且可信赖通常不切实际，在受控环境之外完全不可行。我们提出解耦纵向联邦学习（DVFL），这是一种面向VFL的分块学习方法。通过使各模型基于自身目标独立训练，DVFL实现了去中心化聚合，并将特征学习与标签监督相隔离。基于这些特性，DVFL具备容错性和安全性。我们实现了DVFL来训练拆分神经网络，实验表明，在多种分类数据集上，该方法的模型性能与VFL相当。