Federated machine learning is a technological framework that allows a machine learning model to be collectively constructed and used through data that is distributed across repositories owned by different organizations or devices. While facilitating the building of federated machine learning models, this framework also aims to preserve privacy, improve security, and meet regulatory requirements concerning data usage.
This standard defines the architectural framework and application guidelines for federated machine learning, including the following:
— Description and defnition of federated machine learning
— The categories of federated machine learning technologies and the application scenarios to which each category applies
— A set of measures concerning the performance evaluation criteria for federated machine learning
— Associated features of federated machine learning that fulfll different regulatory requirements
