Sparse federated learning framework targets auditable circular manufacturing supply chains

A researcher describes building a privacy-preserving machine learning system for circular manufacturing, where competing manufacturers collaboratively learn material-flow representations without exposing raw production data. The problem was prompted by an industrial consortium that also needed regulators to audit model decisions without access to underlying data. The author argues that standard federated averaging is ill-suited because it is communication-heavy, produces opaque representations, and lacks any means of external verification. The proposed approach trains a shared sparse encoder whose sparse top-coordinate updates are the only information exchanged, while each participant keeps its decoder and task head private. Auditability is designed into the architecture from the outset rather than added as logging, with cryptographic proofs intended to support verifiable claims such as recycled-content percentages.