  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 six public datasets validate the efficacy of FedValue for data
valuation in the context of VFL. In addition, we illustrate the practical
utility of FedValue with a case study involving federated movie
recommendations.
