Abstract
While taking account into data privacy protection, federated learning can mine local data knowledge and gather data value, which has been widely concerned by the Smart city and Internet of Things. At present, a large amount of data is generated by the massive edge network in the smart city, but the resources of the edge side are limited. How to reduce the communication overhead between the edge and the centralized cloud server, improve the convergence speed of data model, and avoid resource waste caused by synchronized blocking of federated learning has become the core issue for the integration of federated learning and the Internet of Things in the smart city. For this reason, this paper designs a multi-level federated learning mechanism in the smart city, and uses reinforcement learning agents to select nodes to offset the influence of the non-IID data that is not independent and identically distributed. At the same time, asynchronous non-blocking updating method is used to perform model aggregation and updating of federated learning to release the resources of faster devices and improving the efficiency and stability of federated learning. Finally, simulation results show that the proposed method can improve the efficiency of federated learning tasks in edge network scenarios with a lot of devices in the smart city.
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References
Lim, W.Y.B.: Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 22(3), 2031–2063 (2020)
Custers, B., Sears, A., Dechesne, F., Georgieva, I., Tani, T., Hof, S.V.D.: EU Personal Data Protection in Policy and Practice. TMC Asser Press, Hague (2019)
Gaff, B.M., Sussman, H.E., Geetter, J.: Privacy and big data. Computer 47(6), 7–9 (2014)
Hard, A., Rao, K.: Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)
Yang, Q., Liu, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12–112 (2019)
Majeed, U., Hong, C.S.: FLchain: federated learning via MEC-enabled blockchain network. In: Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4 (2019)
Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)
Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N.H., 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 (2019)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans. Indust. Inf. 16(3), 2134–2143 (2020)
Lu, X., Liao, Y., Lio, P., Hui, P.: Privacy-preserving asynchronous federated learning mechanism for edge network computing. IEEE Access 8, 48970–48981 (2020)
Mcmahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Federated Learning with Non-IID Data (2018)
Zhao, Y., Li, M., Lai, L.: Communication-efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics (2017)
Wang, H., Kaplan, Z., Niu, D., Li, B.: Optimizing federated learning on non-iid data with reinforcement learning. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pp. 1698–1707 (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. Indust. Inf. 16(6), 4177–4186 (2020)
Zhu, H., Jin, Y.: Multi-objective evolutionary federated learning. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1310–1322 (2019)
Kim, H., Park, J., Bennis, M., Kim, S.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2019)
Lim, W.Y.B.: Hierarchical incentive mechanism design for federated machine learning in mobile networks. IEEE Internet Things J. 7(10), 9575–9588 (2020)
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5), 156–165 (2019)
Wang, X., Wang, C., Li, X., Leung, V.C.M., Taleb, T.: Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J. 7(10), 9441–9455 (2020)
Zhan, Y., Li, P., Guo, S.: Experience-driven computational resource allocation of federated learning by deep reinforcement learning. In: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 234–243 (2020)
Ali, M., Mujeeb, A., Ullah, H., Zeb, S.: Reactive power optimization using feed forward neural deep reinforcement learning method: (deep reinforcement learning dqn algorithm). In: Asia Energy and Electrical Engineering Symposium (AEEES), pp. 497–501 (2020)
Zuo, G., Du, T., Lu, J.: Double DQN method for object detection. In: 2017 Chinese Automation Congress (CAC), pp. 6727–6732 (2017)
Hasselt, H.V., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI) (2016)
Acknowledgement
This work is supported by National Key R&D Program of China (2019YFB2102301), the National Natural Science Foundation of China (62072049), Key R&D Program of Hebei Province (20310103D), and Key Project Plan of Blockchain in Ministry of Education of the People’s Republic of China (2020KJ010802).
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Guo, S., Xiang, B., Chen, L., Yang, H., Yu, D. (2022). Multi-level Federated Learning Mechanism with Reinforcement Learning Optimizing in Smart City. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_35
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