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A two-tiered incentive mechanism design for federated crowd sensing

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Abstract

Mobile crowd sensing uses the combined effects of a large number of users to collect, process, and reuse data for different types of applications. Federated Learning enables training a global model without compromising users’ privacy. In this work, we attempt to explore a new distributed sensing and learning paradigm, called Federated Crowd Sensing (FCS). Specifically, we propose a two-tiered incentive mechanism for FCS. First, we design an incentive mechanism in the participant recruitment stage where we consider the heterogeneous network effect, where larger fraction of participants will give potential mobile users an added value, but different users perceive it differently. Second, we design another incentive mechanism in the task result collection stage where we propose a hybrid uploading strategy selected by the users after completing the FCS tasks. Using the proposed algorithm for optimal uploading mechanism, the participants can increase their own utility. The numerical results show that platform can attract more potential mobile users, gain higher utility for platform and participants, and reduce the overall cost.

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Notes

  1. We consider the scenario where each individual makes decision without affecting the aggregate behavior. Hence this representation helps in avoiding to deal with the effect of individual decisions on overall population behavior.

  2. Optimal decay factor selection is not in current scope of this paper, and we leave it for future research.

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Acknowledgements

The work of Fan Li is partially supported by the National Natural Science Foundation of China under Grant no. 62072040. The work of Liehuang Zhu is supported by the National Natural Science Foundation of China under Grant no. 61872041, and the National Natural Science Foundation of China General Technology Basic Research Joint Fund under Grant no. U1836212. The work of Youqi Li is partially supported by the National Natural Science Foundation of China under Grant no. 62102028, and China Postdoctoral Science Foundation under Grant no. 2021M700434. The work of Huijie Chen is partially supported by China Postdoctoral Science Foundation under Grant no. 2021M700302. The corresponding author is Fan Li.

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Authors and Affiliations

  1. School of Cyberspace Science and Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China

    Youqi Li & Liehuang Zhu

  2. School of Computer Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China

    Youqi Li, Fan Li & Kashif Sharif

  3. School of Computer Science, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China

    Huijie Chen

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  1. Youqi Li
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Li, Y., Li, F., Zhu, L. et al. A two-tiered incentive mechanism design for federated crowd sensing. CCF Trans. Pervasive Comp. Interact. 4, 339–356 (2022). https://doi.org/10.1007/s42486-022-00111-8

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