scope:

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 definition 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 fulfill different regulatory requirements

Purpose

Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements such as the European Union's General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. Federated machine learning is expected to promote and facilitate collaborations among multiple parties, some of which are data source owners, such that user privacy and information security are protected. This guide will promote the use of distributed data sources without violating regulations or ethical considerations.