  In a vertical federated learning (VFL) system consisting of a central server
and many distributed clients, the training data are vertically partitioned such
that different features are privately stored on different clients. The problem
of split VFL is to train a model split between the server and the clients. This
paper aims to address two major challenges in split VFL: 1) performance
degradation due to straggling clients during training; and 2) data and model
privacy leakage from clients' uploaded data embeddings. We propose FedVS to
simultaneously address these two challenges. The key idea of FedVS is to design
secret sharing schemes for the local data and models, such that
information-theoretical privacy against colluding clients and curious server is
guaranteed, and the aggregation of all clients' embeddings is reconstructed
losslessly, via decrypting computation shares from the non-straggling clients.
Extensive experiments on various types of VFL datasets (including tabular, CV,
and multi-view) demonstrate the universal advantages of FedVS in straggler
mitigation and privacy protection over baseline protocols.
