IEEE Std 3127-2025 IEEE 基于区块链的联邦机器学习架构框架指南

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STANDARDS

IEEEGuideforanArchitectural

FrameworkforBlockchain‐Based

FederatedMachineLearning

IEEEComputerSociety



Developedbythe

ArtificialIntelligenceStandardsCommittee

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IEEEStd3127™‐2025

IEEE Std 3127™-2025

IEEE Guide for an Architectural

Framework for Blockchain-Based

Federated Machine Learning

Developed by the

Artificial Intelligence Standards Committee

of the

IEEE Computer Society

Approved 12 February 20255

IEEE SA Standards Board

Abstract: Guidance for improving the security auditability and traceability of blockchain-based

federated machine learning is provided in this document. Blockchain-based federated machine

learning helps data owners, producers, consumers, and collaborators to realize multi-party secure

computing while meeting applicable interaction, decentralization, safety, reliability, and robustness

guidelines. Blockchain-based Federated Machine Learning can improve the privacy of data owners,

producers, consumers, and collaborators, and enable those entities to give permission for functions

including the use of data, withdrawing the use of data, and potentially selling data under specified

conditions.

Keywords: blockchain, federated machine learning, FML, IEEE 3127™

1

The Institute of Electrical and Electronics Engineers, Inc.

3 Park Avenue, New York, NY 10016-5997, USA

IEEE is a registered trademark in the U.S. Patent & Trademark Office, owned by The Institute of Electrical and Electronics

Engineers, Incorporated.

PDF: ISBN 979-8-8557-2004-4 STD27811

Print: ISBN 979-8-8557-2005-1 STDPD27811

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For more information, visit https://www.ieee.org/about/corporate/governance/p9-26.html.

No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission

of the publisher.

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标签： #机器

摘要：

本指南详细阐述了 IEEE Std 3127-2025 标准，该标准为基于区块链技术的联邦机器学习系统提供了一种架构框架与设计参考。文档旨在解决传统联邦学习中存在的中心化信任、数据隐私与模型安全等问题，通过引入区块链的分布式账本与智能合约机制，实现训练节点的可信协调与模型的防篡改记录。内容覆盖了系统参考架构、关键功能组件（如数据流程、共识协议与激励机制）、安全要求以及互操作性规范，适用于金融、医疗、物联网等对数据隐私与协同建模有严格合规需求的场景。读者可通过该标准全面理解如何构建去中心化且可审计

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IEEE Std 3127-2025 IEEE 基于区块链的联邦机器学习架构框架指南

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