A Bargaining-based Approach for Feature Trading in Vertical Federated Learning

arXiv:2402.15247v1 Announce Type: new
Abstract: Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered as the data consumer who buys the data party's …

abstract arxiv cs.ai cs.lg cs.ma data data privacy enabling environment feature features federated learning machine machine learning paradigm parties popular privacy production set trading training type