  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.
