摘要：With the rapid development of consumer electronics and communication technology, a large amount of data is generated from end users at the edge of the networks. Modern recommendation systems take full advantage of such data for training their various artificial intelligence (AI) models. However, traditional centralized model training has to transmit all the data to the cloud-based servers, which suffers from privacy leakage and resource shortage. Therefore, mobile edge computing (MEC) combined with federated learning (FL) is considered as a promising paradigm to address these issues. The smart devices can provide data and computing resources for the FL and transmit the local model parameters to the base station (BS) equipped with edge servers to aggregate into a global model. Nevertheless, due to the limited physical resources and the risk of privacy leakage, the users (the owners of the devices) would not like to participate in FL voluntarily. To address this issue, we take advantage of game theory to propose an incentive mechanism based on the two-stage Stackelberg game to inspire users to contribute computing resources for FL. We define two utility functions for the users and the BS, and formulate the utility maximization problem. Through theoretical analysis, we obtain the Nash equilibrium strategy of the users and the Stackelberg equilibrium of the utility maximization problem. Furthermore, we propose a game-based incentive mechanism algorithm (GIMA) to achieve the Stackelberg equilibrium. Finally, simulation results are provided to verify the performance of our GIMA algorithm. The experimental results show that our GIMA algorithm converges quickly, and can achieve higher utility value compared to other incentive methods.

关键字：Training; Recommender systems; Computational modeling; Servers; Data models; Data privacy; Federated learning; recommendation system; mobile edge computing; incentive mechanism; game theory

ISSN号：0098-3063

卷、期、页：卷70期1: 2596-2607

发表日期：2024-02-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE TRANSACTIONS ON CONSUMER ELECTRONICS

参与作者：王名,Zhou, Xiaokang,Yao, Lina,Wang, Shoujin,Qi, Lianyong,Chen, Ying

通讯作者：马博闻

第一作者：黄霁崴

论文类型：期刊论文

论文概要：黄霁崴,马博闻,王名,Zhou, Xiaokang,Yao, Lina,Wang, Shoujin,Qi, Lianyong,Chen, Ying，Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC，IEEE TRANSACTIONS ON CONSUMER ELECTRONICS，2024，卷70期1: 2596-2607

论文题目：Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC