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

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.

💡 Research Summary

This paper presents a comprehensive study on integrating Blockchain and Federated Learning (FL) to address the critical challenge of food waste reduction in the grocery retail sector. The core problem lies in the tension between the need for accurate demand forecasting, which benefits from collaborative data pooling, and the legitimate privacy concerns that prevent retailers from sharing sensitive operational data with competitors.

The research begins by establishing the significant environmental and economic impact of food waste, highlighting the retail sector’s pivotal role. It then introduces Federated Learning as a privacy-preserving machine learning paradigm that enables collaborative model training without raw data exchange. In the proposed system, individual retail stores (clients) train local demand forecasting models on their private historical sales data, which includes features like weekly sales figures, holiday flags, temperature, fuel prices, CPI, and unemployment rates. Instead of sharing this data, they only send the model updates (gradients or parameters) to a central aggregator server.

To address trust, security, and transparency issues inherent in FL systems, the authors enhance the framework with several key technologies. They employ Secure Aggregation Plus (SecAgg+) to prevent the server from inspecting individual client updates, ensuring that only the aggregated model is revealed. Global Differential Privacy is applied to the aggregated updates, adding calibrated noise to protect against inference attacks that might reconstruct training data from the final model. For transparency and integrity, the system integrates an Ethereum-based Blockchain and the InterPlanetary File System (IPFS). After each training round, the global model weights are stored on IPFS, generating a unique Content Identifier (CID). This CID, along with transaction metadata, is immutably recorded on the Blockchain. This allows all participating clients to verify that the model updates they receive are genuine and have not been tampered with by the server.

The experimental evaluation uses the Walmart retail dataset, simulating a network of 45 stores. The performance of the proposed Blockchain-based FL approach is rigorously compared against two baselines: 1) a Standalone scenario where each store trains a model only on its local data, and 2) a Centralized scenario which represents the ideal, privacy-violating case where all data is pooled into a single dataset for training. The results demonstrate that the FL model achieves a Mean Squared Error (MSE) almost equivalent to the Centralized model, significantly outperforming the Standalone models. This finding is crucial as it proves that near-optimal predictive accuracy for demand forecasting can be achieved without compromising data privacy. More accurate forecasts directly enable better inventory management, reducing both stockouts and overstock situations, thereby cutting down on waste, particularly for perishable goods.

In conclusion, the paper successfully demonstrates a viable, secure, and transparent pathway for competitors in the retail sector to collaborate towards the common goal of sustainability. By leveraging the combination of Federated Learning for privacy and Blockchain for trust, the proposed framework breaks down the traditional barriers to data sharing. It offers a practical technological solution that aligns economic efficiency with environmental and social responsibility, contributing directly to the global effort of reducing food waste in supply chains.