Trained models
Each record corresponds to one finalized training job. Open one to inspect the schema, the revealed weights, and the on-chain anchors that bind them together.
A logistic-regression rule. 28 weights, 1 bias.
The training run produces one artifact participants actually use: a small set of learned coefficients that can score future records prepared against the same schema. The accounts and signatures on Solana exist to prove this exact rule came from the agreed inputs and the expected training code.
Each feature has one learned weight. Inputs are normalized into [−1, 1] before training, so bar magnitudes are directly comparable.
A shared model learns across separate private cohorts while every source dataset stays under local control.
Each run outputs a logistic-regression rule: feature weights plus one bias that produce a score for the agreed schema.
Because the model is revealed and hashed, anyone can check that the published coefficients match the on-chain record.
AML / suspicious activity detection
1 row per party, 1 epoch, 13 trained features over a 28-feature AML schema.
ICU mortality
Row2 devnet proof: 2 encrypted rows per party, 1 epoch, 4 trained features, 28-weight output shape.
Cybersecurity threat detection
1 row per party, 1 epoch, 13 trained features over a 28-feature threat schema.