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IEEE 3187-2024

IEEE Guide for Framework for Trustworthy Federated Machine Learning

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IEEE 3187-2024

IEEE Guide for Framework for Trustworthy Federated Machine Learning

PUBLISH DATE 2024

PAGES 50

IEEE 3187-2024

Document Information

New IEEE Standard - Active. The development and application of federated machine learning are facing the critical challenges of balancing the tradeoff among privacy, security, performance, and efficiency, how to realize supervision covering the whole life cycle, and how to get explainable results. Thus, trustworthy federated machine learning is proposed to solve the above problem. In this standard, a general view of framework for trustworthy federated machine learning is provided in four parts: a principle in trustworthy federated machine learning, requirements from the perspective of different principles and different federated machine learning participants, and methods to realize trustworthy federated machine learning. Also provided is guidance on how trustworthy federated machine learning is used in various scenarios.

This document provides a reference framework for trustworthy federated machine learning, including the principles of trustworthy federated machine learning, requirements for different roles and principles of trustworthy federated machine learning, and several technologies to realize trustworthy federated machine learning. It also lists some scenarios where trustworthy federated machine learning can be applied.

The purpose of this guide is to provide a credible, practical, and controllable solution guidance for trustworthy federated machine learning and other privacy computing applications.


SDO	IEEE: Institute of Electrical and Electronics Engineers
Document Number	3187
Publication Date	Dec. 19, 2024
Language	en - English
Page Count	50
Revision Level
Supercedes
Committee	Artificial Intelligence Standards Committee

Publish Date	Document Id	Type	View
Dec. 19, 2024	3187-2024	Revision

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IEEE 3187-2024

IEEE Guide for Framework for Trustworthy Federated Machine Learning

IEEE 3187-2024

Document Information

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