Model improvement using federated learning and canonical feature mapping

A method for improving a model based on augmenting a plurality of models trained on private feature data, the method comprising:

• receiving, by one or more processors, an assignment of a domain for an existing model of a model type;
• receiving, by the one or more processors, a seed model that includes a canonical schema associated with the domain of the existing model;
• training, by the one or more processors, the seed model by generating a feature mapper that maps private feature data of the existing model to the canonical schema of the seed model;
• confirming, by the one or more processors, completion of mapping of the private feature data of the existing model to the canonical schema of the seed model;
• receiving, by the one or more processors, a base model including the canonical schema of the seed model;
• training, by the one or more processors, the base model by applying the private feature data of the existing model to the canonical schema of inputs by use of the feature mapper;
• sending, by the one or more processors, the base model that is trained to a model augmentation service, wherein the model augmentation service generates an augmented model by applying federated learning to a plurality of received base models;
• and
• prepending, by the one or more processors, the feature mapper to the augmented base model received from the model augmentation service, wherein the private feature data of the existing model is applied to the feature mapper.

A computer system for generating an artificial intelligence (AI) model, the method comprising:

• one or more computer processors;
• one or more computer readable storage media;
• and
• program instructions stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to receive information associated with respective existing models from a plurality of clients;
• program instructions to group the respective existing models from the plurality of clients into domains based on the received information of the respective existing models;
• program instructions to send a seed model to a first set of clients of the plurality of clients that correspond to existing models that are grouped into a first domain;
• program instructions to receive confirmation of mapping feature data of the respective existing models of the first set of clients to a canonical schema of the seed model;
• program instructions to send a base model with the canonical schema associated with the seed model, respectively, to the first set of clients;
• program instructions to receive from the first set of clients, respectively, the base model that is trained by the feature data of the respective existing models of the first set of clients;
• and
• program instructions to generate an augmented model by combination of attributes from the received base models of respective clients, trained by the feature data of the respective existing models of the first set of clients.