Abstract: A system and method for generating synthetic datasets includes receiving, via an application programming interface (API) of a remote generative database service, a generative database query for obtaining synthetic data samples statistically representative of a sensitive dataset, searching a generative model data structure comprising a plurality of generative model nexuses based on a generative model election request derived from the generative database query, wherein the searching returns a generative model for fulfilling the generative database query, generating a synthetic dataset using the generative model returned from the searching based on a plurality of generative query parameters extracted from the generative database query, and returning the synthetic dataset as a result to the generative database query.

Abstract: A system and method for generating synthetic datasets includes receiving, via an application programming interface (API) of a remote generative database service, a generative database query for obtaining synthetic data samples statistically representative of a sensitive dataset, searching a generative model data structure comprising a plurality of generative model nexuses based on a generative model election request derived from the generative database query, wherein the searching returns a generative model for fulfilling the generative database query, generating a synthetic dataset using the generative model returned from the searching based on a plurality of generative query parameters extracted from the generative database query, and returning the synthetic dataset as a result to the generative database query.

Abstract: A system and method for generating synthetic datasets includes receiving, via an application programming interface (API) of a remote generative database service, a generative database query for obtaining synthetic data samples statistically representative of a sensitive dataset, searching a generative model data structure comprising a plurality of generative model nexuses based on a generative model election request derived from the generative database query, wherein the searching returns a generative model for fulfilling the generative database query, generating a synthetic dataset using the generative model returned from the searching based on a plurality of generative query parameters extracted from the generative database query, and returning the synthetic dataset as a result to the generative database query.

Abstract: A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.

Abstract: A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.