Abstract
Federated learning (FL) has been proposed and applied in edge computing scenarios. However, the complex edge environment of wireless networks, such as limited device computing resources and unstable signals, leads to increase communication overhead and reduced performance for federated learning. Therefore, we propose a hierarchical aggregation mechanism to improve federated learning performance in a resource-constrained wireless edge environment. We propose three feature models to quantify the FL performance and design a fuzzy \(\mathcal {K}\)-means clustering mechanism. We construct an optimization problem for the process of hierarchical aggregation. And a cluster-based hierarchical federated learning algorithm (CluHFed) is designed, which consists of fuzzy clustering, asynchronous aggregation, and topology reconstruction. At last, we make an experiment with Pytorch. The results show that the proposed algorithm improves the accuracy by 2.6%–35.8% and reduces the latency of FL networks by 5.9% compared with other popular federated learning algorithms.
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Acknowledgments
This work was supported by the National Key R &D Program of China (2020YFB2104503) and the National Natural Science Foundation of China (No. 62071070).
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Chen, J., Li, W., Yang, G., Qiu, X., Guo, S. (2022). Federated Learning Meets Edge Computing: A Hierarchical Aggregation Mechanism for Mobile Devices. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_38
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DOI: https://doi.org/10.1007/978-3-031-19211-1_38
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