Vertical Federated Learning (VFL), which has a broad range of real-world
applications, has received much attention in both academia and industry.
Enterprises aspire to exploit more valuable features of the same users from
diverse departments to boost their model prediction skills. VFL addresses this
demand and concurrently secures individual parties from exposing their raw
data. However, conventional VFL encounters a bottleneck as it only leverages
aligned samples, whose size shrinks with more parties involved, resulting in
data scarcity and the waste of unaligned data. To address this problem, we
propose a novel VFL Hybrid Local Pre-training (VFLHLP) approach. VFLHLP first
pre-trains local networks on the local data of participating parties. Then it
utilizes these pre-trained networks to adjust the sub-model for the labeled
party or enhance representation learning for other parties during downstream
federated learning on aligned data, boosting the performance of federated
models.

垂直联邦学习（VFL）在学术界和工业界引起了广泛关注，它具有广泛的实际应用。本文提出了一种新颖的 VFL 混合本地预训练（VFLHLP）方法，通过预训练本地网络来调整标记方的子模型或增强其他方在对齐数据上的表示学习，以提高联邦模型的性能。