  We introduce DaiMoN, a decentralized artificial intelligence model network,
which incentivizes peer collaboration in improving the accuracy of machine
learning models for a given classification problem. It is an autonomous network
where peers may submit models with improved accuracy and other peers may verify
the accuracy improvement. The system maintains an append-only decentralized
ledger to keep the log of critical information, including who has trained the
model and improved its accuracy, when it has been improved, by how much it has
improved, and where to find the newly updated model. DaiMoN rewards these
contributing peers with cryptographic tokens. A main feature of DaiMoN is that
it allows peers to verify the accuracy improvement of submitted models without
knowing the test labels. This is an essential component in order to mitigate
intentional model overfitting by model-improving peers. To enable this model
accuracy evaluation with hidden test labels, DaiMoN uses a novel learnable
Distance Embedding for Labels (DEL) function proposed in this paper. Specific
to each test dataset, DEL scrambles the test label vector by embedding it in a
low-dimension space while approximately preserving the distance between the
dataset's test label vector and a label vector inferred by the classifier. It
therefore allows proof-of-improvement (PoI) by peers without providing them
access to true test labels. We provide analysis and empirical evidence that
under DEL, peers can accurately assess model accuracy. We also argue that it is
hard to invert the embedding function and thus, DEL is resilient against
attacks aiming to recover test labels in order to cheat. Our prototype
implementation of DaiMoN is available at https://github.com/steerapi/daimon.
