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In recent years, the increasing demand for data privacy has positioned federated learning (FL) as a promising approach for collaborative machine learning, allowing participants to preserve privacy while jointly training models. Vertical federated learning (VFL), where participants hold distinct feature sets for the same data cohort, introduces unique privacy challenges. While VFL prevents the sharing of raw data, intermediate results such as the gini coefficient can still reveal sensitive information. We presents a privacy-preserving feature selection framework tailored for VFL, designed to prevent the server from inferring the client’s feature distribution through intermediate computation parameters. By integrating homomorphic encryption (HE) and Differential Privacy (DP), the framework enables collaborative computation without exposing raw data, while the added noise enhances data privacy protection. Experimental evaluations demonstrate that the proposed feature selection method FPFS consistently achieves superior accuracy and efficiency across various datasets, particularly excelling in effective feature selection and privacy preservation. Compared to other methods, FPFS maintains higher model performance across multiple experimental scenarios and effectively mitigates both internal and external attacks.
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