Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting

2025-07-02

International Symposium on Computers and Communications

AI Summary

This study addresses the challenge posed by data privacy barriers that impede collaboration among retailers, leading to inaccurate demand forecasting and increased food waste. To overcome this, the paper proposes the first collaborative framework integrating blockchain and federated learning, enabling multiple parties to jointly train a high-accuracy demand prediction model without sharing raw data. The approach effectively balances privacy preservation with collaborative efficiency, achieving predictive performance in sustainable retail settings that closely approximates the ideal scenario of full data sharing. Experimental results demonstrate that the proposed method significantly outperforms isolated model training, thereby reducing food waste and enhancing supply chain operational efficiency.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• demand forecasting
• food waste
• data privacy
• collaborative learning
• sustainable retail

Innovation

• Federated Learning
• Blockchain
• Demand Forecasting
• Sustainable Supply Chain
• Food Waste Reduction