Privacy-Preserving Asynchronous Vertically Federated Learning against Batch Level Label Inference Attacks
Abstract
Vertical Federated Learning (VFL), which enables multiple parties that hold different features of the same data set to collaboratively train a global learning model without sharing their raw data, is an important paradigm in FL. However, VFL is vulnerable to privacy threats, particularly Label Inference Attacks (LIA), where passive participants try to deduce sensitive labels of the active party. Existing defense strategies, such as Homomorphic Encryption (HE) and Differential Privacy (DP), often face challenges related to high computational overhead or tradeoff between privacy and accuracy, especially in asynchronous settings. Considering this, in this paper, we propose a novel defense mechanism against batch-level label inference attacks (BLI) in vertical asynchronous federated learning (VAFL), where client-level differential noise is added to obscure batch-level gradients such that the passive participants cannot derive the label information from the active participant. Besides, to address party's asynchronicity, our approach incorporates a temporal weight decay strategy according to the staleness of each party. A series numerical simulations are then performed to verify the effectiveness and robustness in privacy protection for VAFL scenarios.
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