Federated Data Sharing Models for Privacy-Preserving Collaboration and Interoperability in Distributed Supply Chain Ecosystems

Rutherford Willliams

Federated Data Sharing Models for Privacy-Preserving Collaboration and Interoperability in Distributed Supply Chain Ecosystems

Authors

Rutherford Willliams

Logistics Management, United Kingdom.

Keywords:

Federated Learning, Federated Data Sharing, Supply Chain, Privacy-Preserving, Secure Multi-Party Computation, Differential Privacy, Interoperability, Data Governance, Decentralized Systems

Synopsis

This short paper examines federated data sharing models as a privacy-preserving approach to enable collaboration and interoperability across distributed supply chain ecosystems. We define federated architectures, compare design patterns (centralized coordinator, fully decentralized peer federation, and hybrid federations), and evaluate privacy, interoperability, and governance trade-offs. Two tables compare federated architectures and privacy techniques; a conceptual diagram shows architecture and data flows. The paper also discusses incentives, standardization, secure computation, and deployment pathways. Practical recommendations and research gaps are presented for industry practitioners seeking to adopt federated data sharing while preserving competitive confidentiality.

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