用区块链联邦学习帮零售商协作预测需求，减少食品浪费。
Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
- 通过区块链实现多方不共享数据的联合建模
- 模型性能接近数据完全共享的理想情况，显著优于单店独立建模
- 适合关注可持续零售与隐私保护的从业者
有效的需求预测对减少食物浪费至关重要。然而，数据隐私问题常阻碍零售商之间的合作，限制了预测准确性的提升。本文探讨联邦学习（FL）在可持续供应链管理（SSCM）中的应用，聚焦易腐商品的生鲜零售领域。我们首先构建了孤立零售商场景下的基础需求预测与浪费评估模型。随后，提出一种基于区块链的联邦学习框架，实现多零售商间无需直接共享数据的协同训练。初步结果显示，该方法的性能几乎等同于各方完全共享数据的理想情况，显著优于各自独立建模的方案，有效降低浪费并提升运营效率。
原文摘要 · Abstract (English)
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.
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