A computer-implemented method practiced on a client device, comprising: collecting private user data on the client device based on user interaction with an application; selecting a model from a set of proxy models on the client device associated with the application on the client device, wherein each model in the set of proxy models was generated by a remote server using crowd-sourced data including at least one of anonymized data, de-identified data, or voluntarily-provided data of identified users, collected on the remote server for the application; performing training of the selected model using the collected private user data that is associated with the user interaction with the application; determining a proxy model that is a closest match to the trained selected model including performing a divergence estimate between the trained selected model and the one or more proxy other models, and selecting among the one or more proxy models, a closest match to the trained selected model as the proxy model having a least divergence estimate from the trained selected model; in response to the least divergence estimate between the trained selected model and the proxy model having the least divergence estimate being less than a threshold: associating the proxy model having the least divergence estimate with the application for using the proxy model having the least divergence estimate as a user predictive model for the application to calibrate functionality of the application on the client device to the user, the user predictive model calibrating a speech recognition module to the user, and the user predictive model incorporating feature vectors including components comprising a language in which the user is speaking, and mel-frequency cepstral coefficients. collecting private user data on the client device based on user interaction with an application; selecting a model from a set of proxy models on the client device associated with the application on the client device, wherein each model in the set of proxy models was generated by a remote server using crowd-sourced data including at least one of anonymized data, de-identified data, or voluntarily-provided data of identified users, collected on the remote server for the application; performing training of the selected model using the collected private user data that is associated with the user interaction with the application; determining a proxy model that is a closest match to the trained selected model including performing a divergence estimate between the trained selected model and the one or more proxy other models, and selecting among the one or more proxy models, a closest match to the trained selected model as the proxy model having a least divergence estimate from the trained selected model; in response to the least divergence estimate between the trained selected model and the proxy model having the least divergence estimate being less than a threshold: associating the proxy model having the least divergence estimate with the application for using the proxy model having the least divergence estimate as a user predictive model for the application to calibrate functionality of the application on the client device to the user, the user predictive model calibrating a speech recognition module to the user, and the user predictive model incorporating feature vectors including components comprising a language in which the user is speaking, and mel-frequency cepstral coefficients.