Research on LSTM-Based Industrial Added Value Prediction Under the Framework of Federated Learning
- 10.2991/978-94-6463-034-3_44How to use a DOI?
- Federated learning; Industrial added value; LSTM
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.
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Cite this article
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 -