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IEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learning
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:
To provide an architectural framework and application guidelines for blockchain-based federated machine learning (BC-FML), including the following:
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:
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