Federated AI for Multi-Enterprise Supply Chain Collaboration

Abstract

As global supply chains expand into complex, interdependent ecosystems, organizations face a paradox: the need to share intelligence without sharing data. Traditional centralized AI architectures, dependent on aggregated datasets, conflict with regulatory restrictions, competitive secrecy, and privacy mandates. Federated Artificial Intelligence (FAI) resolves this paradox by enabling multiple enterprises to collaboratively train and deploy AI models across distributed data sources without transferring raw data. This article proposes an Enterprise Federated AI Architecture (EFAIA) tailored for multi-enterprise supply chain optimization. It integrates data fabric layers, secure aggregation protocols, and governance mechanisms that enable AI-driven forecasting, risk management, and procurement collaboration across organizational boundaries. The framework is demonstrated through industry-relevant use cases in manufacturing, pharmaceuticals, and logistics. Key benefits—enhanced prediction accuracy, regulatory compliance, and ecosystem trust—are balanced against challenges in interoperability, latency, and model governance. The article argues that federated AI is not merely a technical innovation but an organizational catalyst for co-intelligent supply networks, aligning efficiency with ethics in the emerging era of distributed intelligence.