  Most work in privacy-preserving federated learning (FL) has focused on
horizontally partitioned datasets where clients hold the same features and
train complete client-level models independently. However, individual data
points are often scattered across different institutions, known as clients, in
vertical FL (VFL) settings. Addressing this category of FL necessitates the
exchange of intermediate outputs and gradients among participants, resulting in
potential privacy leakage risks and slow convergence rates. Additionally, in
many real-world scenarios, VFL training also faces the acute issue of client
stragglers and drop-outs, a serious challenge that can significantly hinder the
training process but has been largely overlooked in existing studies. In this
work, we present vFedSec, a first dropout-tolerant VFL protocol, which can
support the most generalized vertical framework. It achieves secure and
efficient model training by using an innovative Secure Layer alongside an
embedding-padding technique. We provide theoretical proof that our design
attains enhanced security while maintaining training performance. Empirical
results from extensive experiments also demonstrate vFedSec is robust to client
dropout and provides secure training with negligible computation and
communication overhead. Compared to widely adopted homomorphic encryption (HE)
methods, our approach achieves a remarkable > 690x speedup and reduces
communication costs significantly by > 9.6x.
