Communication efficient machine learning of data across multiple sites

An apparatus, comprising:

• one or more network interfaces to communicate with a network;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• receive machine learning-based generative models from a plurality of distributed sites, wherein each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model;
• receive, from each of the distributed sites, a subset of labeled data observed at that site;
• use the generative models to generate synthetic unlabeled data;
• and
• train a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a service to execute a process comprising:

• receiving, at the service, machine learning-based generative models from a plurality of distributed sites, wherein each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model;
• receiving, at the service and from each of the distributed sites, a subset of labeled data observed at that site;
• using, by the service, the generative models to generate synthetic unlabeled data;
• and
• training, by the service, a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.