Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method is a scholarly work, published in 2024 in ''SSRN Electronic Journal''. The main subjects of the publication include information privacy, business, valuation, Internet privacy, customer value, differential privacy, and computer science. In response, authors 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.