Federated Learning for Privacy-Preserving Apparel Supply Chain Analytics

The apparel industry operates through highly complex and globalized supply chains, where effective data analytics plays a critical role in improving demand forecasting, inventory management, logistics coordination, and sustainability practices. However, organizations within the supply chain are often reluctant to share sensitive data due to concerns about privacy, security, compliance, and competitive risks. Traditional centralized analytics approaches exacerbate these concerns by requiring raw data aggregation, thereby increasing the likelihood of breaches and loss of confidentiality. Federated Learning (FL) has emerged as a transformative paradigm that addresses these challenges by enabling decentralized model training without the need to exchange raw data. In this study, we investigate the application of federated learning to apparel supply chain analytics, with a focus on balancing data utility and privacy preservation. We present a framework that integrates federated optimization, secure aggregation, and differential privacy to allow suppliers, manufacturers, distributors, and retailers to collaboratively train robust predictive models while maintaining strict data sovereignty. Our experimental evaluation demonstrates that federated models achieve comparable or superior forecasting accuracy relative to centralized approaches, while significantly reducing privacy risks. Moreover, results indicate notable improvements in demand forecasting, trend identification, and cost optimization tasks across heterogeneous datasets. By reducing data silos, federated learning fosters stronger collaboration, enhances supply chain resilience, and supports sustainability objectives. Overall, this work provides a practical pathway for implementing privacy-preserving analytics in apparel supply chains through federated learning.