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7 March 2022 An incentive-driven edge intelligence based on Stackelberg game
ZhaoHang Wang, GeMing Xia, Jian Chen
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 1216707 (2022) https://doi.org/10.1117/12.2629121
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
Abstract
Compared with cloud computing, edge computing shows real-time, security and other advantages when dealing with the explosive growth of the Internet of Things. In the process of data processing and mining data value, edge intelligence combines the advantages of edge computing and artificial intelligence to become a research hotspot. Federated learning is one of the most popular machine learning frameworks for edge intelligence. It provides stronger privacy protection for the client through the aggregation of the server-side client-side model. However, the latter often do not participate in the federal learning system due to resource constraints and economic considerations. Therefore, incentive mechanisms are necessary to attract higher quality clients. To this end, we use the Stackelberg game model to model the server and the client, and establish an incentive-driven federated learning algorithm FEDID. Finally, we verified the effectiveness of the incentive mechanism through experiments.
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ZhaoHang Wang, GeMing Xia, and Jian Chen "An incentive-driven edge intelligence based on Stackelberg game", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 1216707 (7 March 2022); https://doi.org/10.1117/12.2629121
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KEYWORDS
Clouds
Artificial intelligence
Computer security
Machine learning
Network security
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