TMI-VFL: Secure Vertical Federated Learning via Threshold Multi-Identity Homomorphic Encryption

Abstract

Vertical Federated Learning (VFL) enables multiple participants to collaboratively train models using vertically partitioned data. However, during the actual training process, participants must exchange intermediate model representations(e.g., embeddings), which creates a potential attack surface for privacy leakage. Recent studies have shown that the URVFL attack achieves precise and covert data reconstruction by constructing malicious gradients and training a decoder using label information, posing a serious threat to the privacy security of vertical federated learning systems.To address this issue, we propose TMI-VFL, a secure training framework based on Threshold Multi-Identity Fully Homomorphic Encryption.This method establishes a ciphertext computation mechanism that ensures embedding vectors, gradients, and intermediate activation values are all processed in encrypted form, while the threshold decryption scheme prevents any single participant from recovering sensitive information. Experimental results show that under URVFL attacks, the proposed method increases reconstruction error by more than 10-fold, significantly reducing the effectiveness of the attack. Meanwhile, model accuracy decreases by less than 1% and remains close to baseline levels. These results indicate that TMI-VFL achieves an effective trade-off between privacy protection and model utility, providing a practical solution for secure VFL.