Apparatus and method for data learning system based on a secured modeling exchange

A method for building a global machine learning (ML) model, the method comprising:

• identifying a group of clients to build a global model by federated learning;
• supplying a model definition for the global model to the group;
• aggregating model parameters received from the group to build the global model, the model parameters being generated by the clients training a local ML model at their respective client sites using local data at their respective client sites;
• and
• calculating an incentive to each client communicably coupled to the platform based on said each client's contribution to train the global model, each client's contribution including one or more model parameters generated as a result of training their local ML model with local data.

A non-transitory computer readable storage media having instructions stored thereupon that, when executed by a processor of a computing system, the instructions cause the computing system to perform operations for building a global machine learning (ML) model, the method comprising:

• identifying a group of clients to build a global model by federated learning;
• supplying a model definition for the global model to the group;
• aggregating model parameters received from the group to build the global model, the model parameters being generated by the clients training a local ML model at their respective client sites using local data at their respective client sites;
• and
• calculating an incentive to each client communicably coupled to the platform based on said each client's contribution to train the global model, each client's contribution including one or more model parameters generated as a result of training their local ML model with local data.