Personalized machine learning on mobile computing devices

A system comprising:

• at least one processor;
• and
• at least one memory including code which when executed by the at least one processor causes operations comprising:
• receiving a configuration for a machine learning model, the configuration comprising a plurality of weights determined by a server during a first phase training of the machine learning model;
• initiating, a second phase of training of the machine learning model using local training data at the system to personalize the machine learning model to a user of a user equipment without updating the plurality of weights of the machine learning model, wherein the local training data is applied to the machine learning model to generate at least a first reference embedding mapped to a label, wherein the first reference embedding and the label are stored in a dictionary at the system;
• in response to receiving a first unknown sample at the machine learning model, using, by the system, the machine learning model to perform a first inference task by generating a first embedding that is used to query the dictionary to find at least the first reference embedding and the label that identifies the first unknown sample;
• in response to a condition at the system being satisfied, triggering, by the system, a third phase of training of the machine learning model using at least the local training data at the system to update the plurality of weights of the machine learning model and to further personalize the machine learning model to the user of the system;
• and
• in response to receiving a second unknown sample at the machine learning model, using, by the system, the machine learning model with the updated weights to perform a second inference task by generating a second embedding to query the dictionary to find a second reference embedding and a corresponding label that identifies the second unknown sample.