Active Standard

IEEE 3127-2025

IEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learning

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.

Standard Committee
C/AISC - Artificial Intelligence Standards Committee
Joint Sponsors
C/LT
C/BDL
Status
Active Standard
PAR Approval
2021-11-09
Board Approval
2025-02-12
History
Published:
2025-04-16

Working Group Details

Society
IEEE Computer Society
Standard Committee
C/AISC - Artificial Intelligence Standards Committee
Working Group
BFML - Blockchain-based Federated Machine Learning
IEEE Program Manager
Christy Bahn
Contact Christy Bahn
Working Group Chair
Ye Ouyang

Other Activities From This Working Group

Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.


No Active Projects

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No Active Standards

These standards have been replaced with a revised version of the standard, or by a compilation of the original active standard and all its existing amendments, corrigenda, and errata.


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These standards have been removed from active status through a ballot where the standard is made inactive as a consensus decision of a balloting group.


No Inactive-Withdrawn Standards

These standards are removed from active status through an administrative process for standards that have not undergone a revision process within 10 years.


No Inactive-Reserved Standards
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