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3127-2025 : IEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learning / Institute of Electrical and Electronics Engineers (IEEE).
- Format:
- Book
- Author/Creator:
- Institute of Electrical and Electronics Engineers (IEEE), author, issuing body.
- Language:
- English
- Subjects (All):
- Blockchains (Databases).
- Software architecture.
- Machine learning.
- Physical Description:
- 1 online resource(40 pages)
- Place of Publication:
- New York, New York : Institute of Electrical and Electronics Engineers (IEEE), 2025.
- Summary:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 979-88-557-2004-4
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