A Bubble-Cluster Federated Learning Framework for Privacy-Preserving Demand Forecasting on Heterogeneous Retail Data

Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by factors such as varying consumer behavior, poses challenges to the effectiveness of federated learning. To tackle this challenge, we propose Bubble-Cluster Federated Learning (BFL), a novel clustering-based federated learning framework tailored for sales prediction. By leveraging differential privacy and feature importance distribution, BFL groups retailers into distinct "bubbles", each forming its own federated learning (FL) system to effectively isolate data heterogeneity. Within each bubble, Transformer models are designed to predict local sales for each client. Our experiments demonstrate that BFL significantly surpasses FedAvg and outperforms local learning in demand forecasting performance across all participating clients. Compared to local learning, BFL can achieve a 5.4\% improvement in R\textsuperscript{2}, a 69\% reduction in RMSE, and a 45\% decrease in MAE. Our study highlights BFL's adaptability in enabling effective federated learning through dynamic adjustments to noise levels and the range of clients participating in each bubble. This approach strategically groups participants into distinct "bubbles" while proactively identifying and filtering out risky clients that could compromise the FL system. The findings demonstrate BFL's ability to enhance collaborative learning in regression tasks on heterogeneous data, achieving a balance between forecasting accuracy and privacy preservation in retail applications. Additionally, BFL's capability to detect and neutralize poisoned data from clients enhances the system's robustness and reliability, ensuring more secure and effective federated learning.