Federated Multi-Agent Learning for Collaborative Supply Chain Optimization with Privacy Preservation

Abstract

The rapid evolution of global supply chain networks has necessitated innovative approaches to address the dual challenges of collaborative optimization and data privacy preservation. This paper proposes a novel framework that integrates Federated Learning (FL) with Multi-Agent Systems (MAS) to enable privacy-preserving collaborative optimization across decentralized supply chain entities. The increasing interconnectivity of modern supply chains creates opportunities for performance enhancement through data-driven decision-making, yet conventional centralized approaches face significant barriers related to data sovereignty, competitive sensitivity, and regulatory compliance. Our proposed Federated Multi-Agent Learning (FMAL) framework addresses these challenges by enabling distributed learning where individual supply chain participants maintain complete control over their proprietary data while contributing to collective intelligence. Through the synergistic combination of FL protocols and MAS coordination mechanisms, the framework facilitates secure model training across heterogeneous supply chain nodes without requiring raw data exchange. The methodology incorporates differential privacy mechanisms, secure aggregation protocols, and adaptive consensus algorithms to ensure robust privacy guarantees while maintaining optimization efficacy. Experimental validation demonstrates that the FMAL framework achieves comparable performance to centralized approaches while providing quantifiable privacy protection, with communication overhead reduced by approximately 23% compared to traditional secure multi-party computation methods. The results indicate significant improvements in inventory optimization, demand forecasting accuracy, and inter-organizational coordination, with privacy budget management ensuring compliance with stringent data protection requirements.