斯塔克伯格竞赛
计算机科学
收入
订单(交换)
数据建模
运筹学
数据科学
数据挖掘
业务
微观经济学
数据库
经济
财务
工程类
作者
Zhixian Zhang,Xinchao Li,Shiyou Yang
标识
DOI:10.1109/iccc55456.2022.9880795
摘要
Federated Learning (FL) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. As a critical category of FL, Vertical federated learning (VFL) is mainly used to model heterogeneous data from multiple parties. In order to scientifically and fairly distribute the revenue of federated participants in VFL, this paper provides a scientific and fair-minded data pricing method based on the contribution of participants for federated models. Firstly, a fair and accurate measurement method of the contribution of each federated participant is provided based on shapely values. On this basis, a data pricing model based on Stackelberg with the hosts as the leader and the guest as the follower is formulated in VFL. The numerical solutions for the data-pricing model indicate that it outperforms traditional data pricing methods such as query-based fixed pricing. These results also provide managerial guidance on contribution measurement, data pricing, and revenue distribution for FL platform owners.
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