A Distributed Elastic Net Regression Algorithm for Private Data Analytics in Internet of Things

Weiwei FANG; Mengran LIU; Yunpeng WANG; Yangyang LI; Zhulin AN

doi:10.11999/JEIT190739

Volume 42 Issue 10

Oct. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2403-2411

Weiwei FANG, Mengran LIU, Yunpeng WANG, Yangyang LI, Zhulin AN. A Distributed Elastic Net Regression Algorithm for Private Data Analytics in Internet of Things[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2403-2411. doi: 10.11999/JEIT190739

Citation:

Weiwei FANG, Mengran LIU, Yunpeng WANG, Yangyang LI, Zhulin AN. A Distributed Elastic Net Regression Algorithm for Private Data Analytics in Internet of Things[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2403-2411. doi: 10.11999/JEIT190739

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A Distributed Elastic Net Regression Algorithm for Private Data Analytics in Internet of Things

doi: 10.11999/JEIT190739

1.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2.
National Engineering Laboratory for Public Safety Risk Perception and Control, Beijing 100041, China
3.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Funds: Beijing Municipal Natural Science Foundation (L191019), The CERNET Innovation Project (NGII20190308)

Received Date: 2019-09-25
Rev Recd Date: 2020-05-12

Available Online: 2020-05-17

Publish Date: 2020-10-13

Abstract

Abstract

In order to solve the problems caused by the traditional data analysis based on the centralized algorithm in the IoT, such as excessive bandwidth occupation, high communication latency and data privacy leakage, considering the typical linear regression model of elastic net regression, a distributed learning algorithm for Internet of Things (IoT) is proposed in this paper. This algorithm is based on the the Alternating Direction Method of Multipliers (ADMM) framework. It decomposes the objective problem of elastic net regression into several sub-problems that can be solved independently by each IoT node using its local data. Different from traditional centralized algorithms, the proposed algorithm does not require the IoT node to upload its private data to the server for training, but rather the locally trained intermediate parameters to the server for aggregation. In such a collaborative manner, the server can finally obtain the objective model after several iterations. The experimental results on two typical datasets indicate that the proposed algorithm can quickly converge to the optimal solution within dozens of iterations. As compared to the localized algorithm in which each node trains the model solely based on its own local data, the proposed algorithm improves the validity and the accuracy of training models; as compared to the centralized algorithm, the proposed algorithm can guarantee the accuracy and the scalability of model training, and well protect the individual private data from leakage.
- Internet of Things(IoT),
- Privacy protection,
- Elastic net regression,
- Distributed algorithm,
- Alternating Direction Method of Multipliers (ADMM)

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References(15)

References

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