Paper 2025/1451

MUSE-VFL: Multi-party Unified System for Private and Communication Efficient Backpropagation in Vertical Federated Learning

Abstract

Vertical federated learning (VFL) enables a cohort of parties with vertically partitioned data to collaboratively train a machine learning (ML) model without requiring them to centralise their data. Each party feeds its data to its local model, with output fed to a global model. However, this configuration requires parties to share some intermediary results during training, which include the output and the gradients of the local models. These intermediary results can reveal insights into the parties' data, and can be protected by secret sharing them with secure multiparty computation (MPC). However, this increases the total number of communications and makes the VFL training significantly slower. In this work, we introduce MUSE-VFL to accelerate the computation of the local gradients by using homomorphic encryption on top of MPC for parties to directly complete this computation during backpropagation. We show theoretically that MUSE-VFL improves the complexity of the MPC baseline. Our experiments, conducted on four different ML tasks, show that the runtime needed to compute the gradients of the local models significantly outweighs the combined runtime of all other steps. This highlights the significance of MUSE-VFL, with experiments demonstrating a training runtime faster by 30% to 35% for LAN and 32% to 50% for WAN.