Methods and apparatus for distributed use of a machine learning model

A server comprising:

• an interface;
• machine readable instructions;
• and
• at least one processor circuit to execute the machine readable instructions to at least:
• distribute a central model to a first endpoint, a second endpoint, and a third endpoint;
• access a first locally trained model, the first locally trained model created by training the central model at the first endpoint using first local data, the first local data local to the first endpoint;
• access a second locally trained model, the second locally trained model created by training the central model at the second endpoint using second local data, the second local data different from the first local data, the second local data local to the second endpoint;
• access a third model from the third endpoint;
• aggregate the first locally trained model and the second locally trained model to produce a new central model;
• provide the new central model to the first endpoint;
• and
• provide the new central model to the second endpoint.

A method comprising:

• distributing a central model to a first endpoint, a second endpoint, and a third endpoint;
• accessing a first locally trained model, the first locally trained model created by training the central model at the first endpoint using first local data, the first local data local to the first endpoint;
• accessing a second locally trained model, the second locally trained model created by training the central model at the second endpoint using second local data, the second local data different from the first local data, the second local data local to the second endpoint;
• accessing a third model from the third endpoint;
• aggregating, by executing an instruction with at least one processor, the first locally trained model and the second locally trained model to produce a new central model;
• providing the new central model to the first endpoint;
• and
• providing the new central model to the second endpoint.