Vertical Federated Learning (VFL) has emerged as a popular machine learning
paradigm, enabling model training across the data and the task parties with
different features about the same user set while preserving data privacy. In
production environment, VFL usually involves one task party and one data party.
Fair and economically efficient feature trading is crucial to the
commercialization of VFL, where the task party is considered as the data
consumer who buys the data party's features. However, current VFL feature
trading practices often price the data party's data as a whole and assume
transactions occur prior to the performing VFL. Neglecting the performance
gains resulting from traded features may lead to underpayment and overpayment
issues. In this study, we propose a bargaining-based feature trading approach
in VFL to encourage economically efficient transactions. Our model incorporates
performance gain-based pricing, taking into account the revenue-based
optimization objectives of both parties. We analyze the proposed bargaining
model under perfect and imperfect performance information settings, proving the
existence of an equilibrium that optimizes the parties' objectives. Moreover,
we develop performance gain estimation-based bargaining strategies for
imperfect performance information scenarios and discuss potential security
issues and solutions. Experiments on three real-world datasets demonstrate the
effectiveness of the proposed bargaining model.

垂直联邦学习（Vertical Federated Learning）引入了基于交易的训练方法来有效商业化，采用基于性能增益的定价，并在完美和不完美性能信息环境下进行分析和优化模型，研究了潜在的安全问题与解决方案，并通过三个真实数据集的实验证明了提出的交易模型的有效性。