Federated learning privacy incentives:Reverse auctions and negotiations  

作　　者：Hongqin Lyu Yongxiong Zhang Chao Wang Shigong Long Shengnan Guo 

机构地区：[1]Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang,China [2]College of Computer Science and Technology,Guizhou University,Guiyang,China

出　　处：《CAAI Transactions on Intelligence Technology》2023年第4期1538-1557,共20页智能技术学报（英文）

基　　金：National Natural Science Foundation of China,Grant Number:62062020;National Natural Science Foundation of China,Grant Number:72161005;Technology Foundation of Guizhou Province,Grant Number:QianKeHeJiChu‐ZK[2022]‐General184.

摘　　要：The incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy loss.To this end,this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection.Based on the analysis,a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning.The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem.In addition,a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed.In the experimental part,the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees.

关 键 词：artificial intelligence data privacy decision making internet of things 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] F724.59[自动化与计算机技术—计算机科学与技术]