Privacy-enhanced training and deployment of machine learning models using client-side and server-side data

A computer-implemented method, comprising:

• obtaining, by a server and using server-side data obtained from a server-side user, server-side training data that includes a plurality of features and a corresponding plurality of training labels;
• and
• training, by the server, at least part of a decentralized model in a plurality of training rounds, wherein the decentralized model (1) is trained to output data indicating whether one or more digital components should be provided to the client device based on input user activity data and digital component attribute data, and (2) includes a client-side machine learning model and a server-side machine learning model,
• wherein:
• the server-side machine learning model comprises at least a first sub-model corresponding to a content provider and a second sub-model corresponding to a content publisher;
• each of the first sub-model and the second sub-model are configured to generate a respective server-side embedding;
• and
• training, by the server, in each training round of the plurality of training rounds comprises:
• receiving, from a client device, second data including a current client-side embedding generated by the client-side machine learning model, wherein the second data received from the client device does not include any client-side features used in generating the current client-side embedding;
• updating, using server-side embedding and the current client-side embedding and based on the plurality of training labels, the server-side machine learning model;
• generating, using the updated server-side machine learning model, an updated server-side embedding;
• and
• transmitting, by the server and to the client device, first data including the updated server-side embedding for subsequent updating of the client-side machine learning model, wherein updating the server-side machine learning model comprises minimizing a server-side loss function based on a combination of the current client-side embedding and a server-side embedding while parameters of the client-side machine learning model are held fixed.

A computer-implemented method performed by a server, comprising:

• receiving, from a client device, a request for a digital component to be provided for display within a content page displayed on the client device;
• receiving, from the client device, first data including a client-side embedding generated by a trained client-side machine learning model, wherein;
• the server-side machine learning model comprises at least a first sub-model corresponding to a content provider and a second sub-model corresponding to a content publisher;
• each of the first sub-model and the second sub-model are configured to generate a respective server-side embedding;
• and
• the trained client-side machine learning model and a server-side machine learning model jointly define a decentralized model trained to output data indicating whether one or more digital components should be provided to a client device based on input user activity data and digital component attribute data;
• generating, using the server-side machine learning model and based on digital component attribute data relating to a plurality of digital components, a server-side embedding, wherein the digital component attribute data is stored at the server;
• combining the server-side embedding and the client-side embedding to obtain a combined embedding;
• generating, using the combined embedding and for each digital component in the plurality of digital components, a score indicating whether the digital component should be provided to the client device;
• selecting, from among the plurality of digital components and based on the scores for the plurality of digital components, a particular digital component;
• and
• transmitting, to the client device, the particular digital component for provision within the content page displayed on the client device.