IEEE 3187:2024

New IEEE Standard - Active - Draft.
The development and application of federated machine learning are facing the critical challenges about how to balance the tradeoff among privacy, security, performance, and efficiency, how to realize supervision covering the whole life cycle and how to get the explainable results. Then trustworthy federated machine learning is proposed to solve the above problem. In this standard, a general view on 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. It also provides some guidance on how trustworthy federated machine learning is used in various scenarios.

This guide provides a reference framework for trustworthy Federated Machine Learning. The document provides guidance with respect to provable security for data and models, optimized model utility, controllable communication and computational complexity, explainable decision making and supervised processes. The guide describes three main aspects: 1) principles for trustworthy Federated Machine Learning, 2) requirements for different roles in trustworthy Federated Machine Learning, and 3) techniques to realize trustworthy Federated Machine Learning.
The purpose of this guide is to provide credible, practical and controllable solution guidance for Federated Machine Learning and other privacy computing applications.