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.
@article{arxiv.2602.04384,
title = {Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting},
author = {Fabio Turazza and Alessandro Neri and Marcello Pietri and Maria Angela Butturi and Marco Picone and Marco Mamei},
journal= {arXiv preprint arXiv:2602.04384},
year = {2026}
}
Comments
Author-accepted manuscript of a paper published in the IEEE International Symposium on Computers and Communications (ISCC), 2025, pp. 1-6. doi: https://doi.org/10.1109/ISCC65549.2025.11326299