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Vertical Federated learning (VFL) is a promising paradigm for predictiveanalytics, empowering an organization (i.e., task party) to enhance itspredictive models through collaborations with multiple data suppliers (i.e.,data parties) in a decentralized and privacy-preserving way. Despite thefast-growing interest in VFL, the lack of effective and secure tools forassessing the value of data owned by data parties hinders the application ofVFL in business contexts. In response, we propose FedValue, aprivacy-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, namelyMShapley-CMI. The metric evaluates a data party's contribution to a predictiveanalytics task without the need of executing a machine learning model, makingit well-suited for real-world applications of VFL. Next, we develop aninnovative federated computation method that calculates the MShapley-CMI valuefor each data party in a privacy-preserving manner. Extensive experimentsconducted on six public datasets validate the efficacy of FedValue for datavaluation in the context of VFL. In addition, we illustrate the practicalutility of FedValue with a case study involving federated movierecommendations.

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method