Collaborative Machine Learning Markets

We study the problem of collaborative machine learning markets where multiple
parties can achieve improved performance on their machine learning tasks by
combining their training data. We discuss desired properties for these machine
learning markets in terms of fair revenue distribution and potential threats, including
data replication. We then instantiate a collaborative market for cases where parties
share a common machine learning task and where parties’ tasks are different. Our
marketplace incentivizes parties to submit high quality training and true validation
data using a novel payment-division function that is robust-to-replication.