Abstract
Since massive data are generated at the network’s edge, the Internet of Things devices can exploit edge computing and federated learning to train artificial intelligence (AI) models while protecting data privacy. However, heterogeneous devices lead to low efficiency and single-point-of-failure. Moreover, malicious nodes may affect training accuracy. Therefore, we propose FedLyra, an effective blockchain-based asynchronous federated learning architecture, to improve the efficiency of aggregation and resist malicious nodes in a trusted and decentralized manner. We then propose a reputation mechanism that combines historical behaviors and the quality of local updates to resist disagreements and adversaries. With the help of the reputation mechanism, we propose a council-based decentralized aggregation mechanism to exclude malicious nodes. Experiments show that FedLyra can resist malicious nodes and ensure the accuracy of training results.
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- 1.
The CNN network trained on Cifar has 6 layer with the following structure: \(3 \times 3 \times 64/128/256/512\) Convolutional \(\rightarrow \) \(2 \times 2\) MaxPool \(\rightarrow \) 2048 Fully connected \(\rightarrow \) SoftMax.
- 2.
The CNN network trained on Fashion-MNIST has 5 layer with the following structure: \(3 \times 3 \times 16/32/64\) Convolutional \(\rightarrow \) \(2 \times 2\) MaxPool \(\rightarrow \) 576 Fully connected \(\rightarrow \) SoftMax.
References
Ali, M.S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., Rehmani, M.H.: Applications of blockchains in the Internet of Things: a comprehensive survey. IEEE Commun. Surv. Tutor. 21(2), 1676–1717 (2019)
Castro, M., Liskov, B.: Practical byzantine fault tolerance. In: Seltzer, M.I., Leach, P.J. (eds.) Proceedings of the Third USENIX Symposium on Operating Systems Design and Implementation (OSDI), New Orleans, Louisiana, USA, 22–25 February 1999, pp. 173–186. USENIX Association (1999)
Chen, M., Mao, B., Ma, T.: FedSA: a staleness-aware asynchronous Federated Learning algorithm with non-IID data. Future Gener. Comput. Syst. 120, 1–12 (2021)
Chen, Y., Ning, Y., Slawski, M., Rangwala, H.: Asynchronous online federated learning for edge devices with non-IID data. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 15–24 (2020)
Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021)
Feng, L., Zhao, Y., Guo, S., Qiu, X., Li, W., Yu, P.: Blockchain-based asynchronous federated learning for Internet of Things. IEEE Trans. Comput. 1 (2021)
Jin, H., Yan, N., Mortazavi, M.: Simulating aggregation algorithms for empirical verification of resilient and adaptive federated learning. In: 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), pp. 124–133 (2020)
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)
Khan, L.U., et al.: Federated learning for edge networks: resource optimization and incentive mechanism. IEEE Commun. Mag. 58(10), 88–93 (2020)
Kim, H., Park, J., Bennis, M., Kim, S.L.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Sig. Process. Mag. 37(3), 50–60 (2020)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: Proceedings of Machine Learning and Systems 2020, MLSys 2020, Austin, TX, USA, 2–4 March 2020. mlsys.org (2020)
Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., Yan, Q.: A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw. 35(1), 234–241 (2021)
Liu, Y., Qu, Y., Xu, C., Hao, Z., Gu, B.: Blockchain-enabled asynchronous federated learning in edge computing. Sensors 21(10), 3335 (2021)
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017)
McMahan, B., Moore, E., Ramage, D., Hampson, S., Aguera y Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 1273–1282. PMLR, 20–22 April 2017
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., Pedarsani, R.: FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization. In: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 108, pp. 2021–2031. PMLR, 26–28 August 2020
Tak, A., Cherkaoui, S.: Federated edge learning: design issues and challenges. IEEE Netw. 35(2), 252–258 (2021)
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 Netw. 33(5), 156–165 (2019)
Wu, M., Wang, K., Cai, X., Guo, S., Guo, M., Rong, C.: A comprehensive survey of blockchain: from theory to IoT applications and beyond. IEEE Internet Things J. 6(5), 8114–8154 (2019)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. CoRR abs/1708.07747 (2017). http://arxiv.org/abs/1708.07747
Xie, C., Koyejo, S., Gupta, I.: Asynchronous federated optimization. CoRR abs/1903.03934 (2019). http://arxiv.org/abs/1903.03934
Yaga, D., Mell, P., Roby, N., Scarfone, K.: Blockchain technology overview. arXiv preprint arXiv:1906.11078, October 2018
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019)
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This work is supported by National Natural Science Foundation of China (62072049).
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Gao, Z., Li, H., Lin, Y., Chai, Z., Yang, Y., Rui, L. (2022). Effective Blockchain-Based Asynchronous Federated Learning for Edge-Computing. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 460 . Springer, Cham. https://doi.org/10.1007/978-3-031-24383-7_28
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DOI: https://doi.org/10.1007/978-3-031-24383-7_28
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