Federated Learning for Supply Chain Demand Forecasting
Hexu Wang, Fei Xie, Qun Duan, Jing Li
Abstract
With the country’s policy support and the rapid development of Internet technology, the domestic consumption level has been escalating and the consumption structure has changed. The traditional retail industry cannot integrate all the relevant data due to data security and privacy protection concerns so that it is unable to adjust sales strategies in an accurate and timely manner. New retail has sounded the clarion call for the retail revolution. The supply chain demand forecasting is an important problem for the supply chain management. In this research, we propose a new retail supply chain commodity demand forecasting framework based on vertical federal learning, which solves the problems of data security and privacy faced by new retail theoretically and empirically. In experiments, we use datasets from different platforms (such as social platforms, e-commerce platforms, and retailers) in the same region for federated learning. The experiment results demonstrate the superiority of the proposed algorithm.