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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Notes
- 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.
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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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