Abstract
In this paper, we consider a mobile edge computing (MEC) system that multiple users participate in the federated learning protocol by jointly training a deep neural network (DNN) with their private training datasets. The main challenges of applying federated learning to MEC are: (1) it incurs tremendous computational cost by carrying out the deep neural network training phase on the resource-constraint mobile edge devices; (2) existing literature demonstrates that the parameters of a DNN trained on a dataset can be exploited to partially reconstruct the training samples in original dataset. To address the aforementioned issues, we introduce an efficiently private federated learning scheme in mobile edge computing, named FedMEC, with model partition technique and differential privacy method in this work. The experimental results demonstrate that our proposed FedMEC scheme can achieve high model accuracy under different perturbation strengths.
Supported in part by the National Key Research and Development Program of China, under Grant 2017YFB0802303, in part by the National Natural Science Foundation of China, under Grant 61672283, and in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX18_0308.
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Acknowledgment
This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB0802303, in part by the National Natural Science Foundation of China under Grant 61672283 and Grant 61602238, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20160805, and in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX18_0308.
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Zhang, J., Wang, J., Zhao, Y., Chen, B. (2019). An Efficient Federated Learning Scheme with Differential Privacy in Mobile Edge Computing. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_46
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DOI: https://doi.org/10.1007/978-3-030-32388-2_46
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