Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.

翻译：有效的需求预测对于减少食物浪费至关重要。然而，数据隐私问题常常阻碍零售商之间的协作，限制了提升预测准确性的潜力。本研究探讨了联邦学习在可持续供应链管理中的应用，重点关注处理易腐商品的杂货零售领域。我们为孤立零售商场景下的需求预测与浪费评估开发了一个基线预测模型。随后，我们引入了一种基于区块链的联邦学习模型，该模型在多个零售商之间进行协同训练，无需直接共享数据。我们的初步结果表明，联邦学习模型的性能几乎等同于各方相互共享数据的理想设置，并且显著优于各方在不共享数据情况下独立构建的模型，从而减少了浪费并提升了效率。