Abstract
Huge amounts of data produced by millions of IoT devices are expected to be shared and leveraged as the cornerstone of real-world IoT applications, such as industrial IoT, smart grid, and intelligent transportation system. However, there still exist bottlenecks when implementing IoT sharing, such as the privacy leakage of user data, IoT data quality, and incentives for sharing these data. In this paper, we propose an online incentive framework for model sharing based on blockchain and federated learning to improve the privacy protection of IoT data. To ensure the quality of submitted data, a reputation mechanism is further designed to punish the users who do not complete the model-sharing task. Based on these settings, the model sharing problem based on federated learning is formulated as an online incentive mechanism, then we use deep reinforcement learning to obtain optimal sets of sharing users with the goal of maximizing long-term social welfare. Numerical results indicate the effectiveness of the proposed framework and incentive mechanism in IoT sharing.
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References
De Prieëlle, F., De Reuver, M., Rezaei, J.: The role of ecosystem data governance in adoption of data platforms by internet-of-things data providers: case of Dutch horticulture industry. IEEE Trans. Eng. Manage. 69(4), 940–950 (2020)
Chen, W., Qiu, X., Cai, T., Dai, H.N., Zheng, Z., Zhang, Y.: Deep reinforcement learning for internet of things: a comprehensive survey. IEEE Commun. Surv. Tutor. 23(3), 1659–1692 (2021)
Xu, Y., Xiao, M., Wu, J., Zhang, S., Gao, G.: Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: a three-stage Stackelberg game approach. IEEE Trans. Mob. Comput. (2022). https://doi.org/10.1109/TMC.2022.3157687
Gao, G., Xiao, M., Wu, J., Huang, H., Wang, S., Chen, G.: Auction-based VM allocation for deadline-sensitive tasks in distributed edge cloud. IEEE Trans. Serv. Comput. 14(6), 1727–1741 (2021)
Liu, Y., Hao, X., Ren, W., Xiong, R., Zhu, T., Choo, K.K.R., Min, G.: A blockchain-based decentralized, fair and authenticated information sharing scheme in zero trust internet-of-things. IEEE Trans. Comput. 27(2), 501–512 (2023)
Yuan, M., et al.: TRUCON: blockchain-based trusted data sharing with congestion control in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 24(3), 3489–3500 (2023)
Pu, Y., Hu, C., Deng, S., Alrawais, A.: R\(^{2}\)PEDS: a recoverable and revocable privacy-preserving edge data sharing scheme. IEEE Internet Things J. 7(9), 8077–8089 (2020)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans. Industr. Inf. 16(6), 4177–4186 (2019)
Jing, Y., Wang, J., Jiang, C., Zhan, Y.: Satellite MEC with federated learning: architectures, technologies and challenges. IEEE Netw. 36(5), 106–112 (2022)
Brendan McMahan, H., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of Machine Learning Research: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, pp. 1273–1282. PMLR, AISTATS (2017)
Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N., Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 1387–1395. IEEE (2019)
Tu, X., Zhu, K., Luong, N.C., Niyato, D., Zhang, Y., Li, J.: Incentive mechanisms for federated learning: from economic and game theoretic perspective. IEEE Trans. Cogn. Commun. Netw. 8(3), 1566–1593 (2022)
Luo, B., Li, X., Wang, S., Huang, J., Tassiulas, L.: Cost-effective federated learning design. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1–10. IEEE (2021)
Wang, M., Deng, W.: Oracle-MNIST: a realistic image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:2205.09442 (2022)
Chen, W., Chen, Y., Chen, X., Zheng, Z.: Toward secure data sharing for the IoV: a quality-driven incentive mechanism with on-chain and off-chain guarantees. IEEE Internet Things J. 7(3), 1625–1640 (2019)
Acknowledgements
The work described in this paper was supported by the National Natural Science Foundation of China (62302154, 62306108), the Doctoral Scientific Research Foundation of Hubei University of Technology (XJ2022006701), and the Key Research and Development Program of Hubei Province (2023BEB024).
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Cai, T., Li, X., Chen, W., Wei, Z., Ye, Z. (2024). Blockchain-Based Federated Learning for IoT Sharing: Incentive Scheme with Reputation Mechanism. In: Chen, J., Wen, B., Chen, T. (eds) Blockchain and Trustworthy Systems. BlockSys 2023. Communications in Computer and Information Science, vol 1896. Springer, Singapore. https://doi.org/10.1007/978-981-99-8101-4_19
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DOI: https://doi.org/10.1007/978-981-99-8101-4_19
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