PIVODL: Privacy-preserving vertical federated learning over distributed labels

2021/08/25 by Hangyu Zhu, Rui Wang, Zhu, Hangyu +5
Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2108.11444

openalex publication_date 2021/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Non-parametric models like gradient boosting decision trees (GBDT) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this work, we propose a secure vertical FL framework, named PIVODL, to train GBDT with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible.

Related

• FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
• Differentially Private Vertical Federated Learning
• Vertical Federated Learning: Challenges, Methodologies and Experiments
• Vertical federated learning based on DFP and BFGS
• Label Leakage and Protection from Forward Embedding in Vertical Federated Learning