Federated Learning for Supply Chain

Federated learning (FL) is a machine learning paradigm that allows multiple parties to collaboratively train a shared model without exchanging their raw training data. Instead, each participant trains a local model on their own data and shares only model updates (gradients or parameters) with a central aggregator—or, in decentralised variants, directly with peers. This preserves data locality, confidentiality, and regulatory compliance while enabling collective intelligence to emerge from distributed datasets.

In supply chain contexts, federated learning is particularly compelling. Modern supply chains involve dozens or hundreds of firms—buyers, suppliers, logistics providers, financial institutions—each holding operationally sensitive data. Pooling this data centrally is often impossible due to competitive concerns, data protection regulations (e.g., GDPR), and contractual restrictions. Federated learning unlocks the ability to train powerful shared models for demand forecasting, anomaly detection, supplier risk assessment, and collaborative inventory management without any party surrendering proprietary information.

Research in this area addresses key technical challenges including statistical heterogeneity across partners (non-IID data), communication efficiency, robustness to adversarial participants, and privacy guarantees via differential privacy or secure multi-party computation. Contributions from the Supply Chain AI Lab at the University of Cambridge and the broader machine learning community are establishing the foundations for trustworthy, cross-enterprise AI in supply chains.

We invite you to explore the curated collection of key publications below, offering a gateway into this promising field.

List of Publications

1. Zheng, G., Kong, L. and Brintrup, A., 2023. Federated machine learning for privacy preserving, collective supply chain risk prediction. International Journal of Production Research, 61(23), pp.8115-8132. [PDF]
2. Kong, L., Zheng, G. and Brintrup, A., 2024. A federated machine learning approach for order-level risk prediction in supply chain financing. International Journal of Production Economics, 268, p.109095. [PDF]
3. Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G. and MacCarthy, B.L., 2024. Digital supply chain surveillance using artificial intelligence: Definitions, opportunities and risks. International Journal of Production Research, 62(13), pp.4674-4695. [PDF]
4. Kosasih, E.E., Papadakis, E., Baryannis, G. and Brintrup, A., 2024. A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches. International Journal of Production Research, 62(4), pp.1510-1540. [PDF]