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IEEE 3127-2025

IEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learning
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IEEE 3127-2025

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

PUBLISH DATE 2025
PAGES 40
IEEE 3127-2025

New IEEE Standard - Active.

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.

To provide an architectural framework and application guidelines for blockchain-based federated machine learning (BC-FML), including the following:

  • A description and a definition of BC-FML
  • The types for BC-FML
  • Application scenarios for each type
  • A definition of capability for BC-FML and guidelines for evaluating these systems
  • Security and privacy guidelines of BC-FML
  • Performance evaluation of BC-FML in real application systems

The purpose of this document is to provide guidance for improving the security audibility and traceability of BC-FML.

BC-FML helps data owners, coordinators, model users, etc., to realize multi-party federated modeling while meeting applicable interaction, decentralization, safety, reliability, and robustness requirements.

BC-FML can improve the privacy for data owners, coordinators, model users, etc., and enable those entities to permit functions including:

  • the use of data,
  • withdrawing the use of data, and
  • potentially selling data under specified conditions.
SDO IEEE: Institute of Electrical and Electronics Engineers
Document Number 3127
Publication Date April 16, 2025
Language en - English
Page Count 40
Revision Level
Supercedes
Committee Artificial Intelligence Standards Committee
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