Research on LSTM-Based Industrial Added Value Prediction Under the Framework of Federated Learning

DOI

10.2991/978-94-6463-034-3_44How to use a DOI?

Keywords

Federated learning; Industrial added value; LSTM

Abstract

Industrial added value is an important indicator to measure the performance of the real economy. Scientifically predicting industrial added value helps the government to understand the economic situation in a timely and accurate manner, and to formulate practical and reliable economic policies. Existing research shows that the industrial value-added data from the Department of Industry and Information is related to the macroeconomic data within the government and the power big data within the State Grid, and the three parties are independent of each other, data is heterogeneous, and there is a “data island” problem. Therefore, this paper takes Liaoning Province as an example, under the framework of federated learning, using Long Short-Term Memory (LSTM) network to predict the industrial added value data of Liaoning Province. The results show that the method proposed in this study can effectively protect the privacy security between institutions, fully integrate and utilize the data value of each institution, and can more accurately predict the industrial added value.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pan Hu
AU  - Jun Qi
AU  - Jue Bo
AU  - Yu Xia
AU  - Chuan-Ming Jiao
AU  - Meng-Tong Huang
PY  - 2022
DA  - 2022/12/23
TI  - Research on LSTM-Based Industrial Added Value Prediction Under the Framework of Federated Learning
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 426
EP  - 434
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_44
DO  - 10.2991/978-94-6463-034-3_44
ID  - Hu2022
ER  -