Data Valuation for Vertical Federated Learning: A Model-Free and Privacy-Preserving Method
提出FedValue方法，通过新指标MShapley-CMI和联邦计算方法，在保护隐私的前提下评估纵向联邦学习中各数据方的数据价值，无需运行机器学习模型，适用于推荐系统和金融违约预测等场景。
Vertical federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party’s contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on synthetic and realistic datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with case studies involving federated recommendations and financial default prediction.