Federated learning across supply partners

Model complexity should earn its place through better decisions, not novelty. This article explores federated learning across supply partners and the baselines needed to establish its incremental value.

Practitioner lens

Separate prediction from action in federated learning across supply partners: identify what is estimated, what is optimized and which business rules must remain binding regardless of model confidence.

First experiment

Separate model-generated proposals from approved actions; preserve a safe fallback when inputs are missing.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.