斯塔克伯格竞赛
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计算机科学
云计算
对抗制
边缘计算
GSM演进的增强数据速率
机构设计
私人信息检索
信息泄露
边缘设备
差别隐私
数据建模
计算机网络
激励
信息隐私
计算机安全
服务器
人工智能
数据挖掘
数据库
万维网
数理经济学
操作系统
经济
微观经济学
数学
作者
Tianyu Liu,Boya Di,Peng An,Lingyang Song
出处
期刊:IEEE Transactions on Network Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-07-01
卷期号:8 (3): 2588-2600
被引量:17
标识
DOI:10.1109/tnse.2021.3100096
摘要
To avoid the original private data uploading in cloud-edgecomputing, the federated learning (FL) scheme is recently proposed which enhances the privacy preservation. However, the attacks against the uploaded model updates in FL can still cause private data leakage which demotivates the privacy-sensitive participating edge devices. To address this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the privacy-sensitive edge devices are motivated to contribute to the local training and model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We first model the data leakage quantitatively from an adversarial perspective, and then formulate the incentive design problem as a three-layer Stackelberg game, where the interaction between the edge servers and edge devices is further formulated as an optimal contract design problem. Extensive theoretical analysis and numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.
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