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Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

1
School of Information, Beijing Wuzi University, Beijing 101149, China
2
School of Business Administration, Northeast University of Finance and Economics, Dalian 116025, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Yi Zhang, Guowei Hua, Edwin Cheng and Weihua Liu
Sustainability 2021, 13(23), 13050; https://doi.org/10.3390/su132313050
Received: 30 September 2021 / Revised: 17 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies. View Full-Text
Keywords: horizontal federated learning; e-commerce enterprise demand forecasting; time-series analysis; LSTM horizontal federated learning; e-commerce enterprise demand forecasting; time-series analysis; LSTM
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MDPI and ACS Style

Li, J.; Cui, T.; Yang, K.; Yuan, R.; He, L.; Li, M. Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development. Sustainability 2021, 13, 13050. https://doi.org/10.3390/su132313050

AMA Style

Li J, Cui T, Yang K, Yuan R, He L, Li M. Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development. Sustainability. 2021; 13(23):13050. https://doi.org/10.3390/su132313050

Chicago/Turabian Style

Li, Juntao, Tianxu Cui, Kaiwen Yang, Ruiping Yuan, Liyan He, and Mengtao Li. 2021. "Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development" Sustainability 13, no. 23: 13050. https://doi.org/10.3390/su132313050

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