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IEEE Std 3127-2025

contributor authorIEEE - The Institute of Electrical and Electronics Engineers, Inc.
date accessioned2025-09-30T23:08:22Z
date available2025-09-30T23:08:22Z
date copyright16 April 2025
date issued2025
identifier other10965995.pdf
identifier urihttps://lib.yabesh.ir/std/handle/yse/348563
description abstractGuidance 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.
languageEnglish
publisherIEEE - The Institute of Electrical and Electronics Engineers, Inc.
titleIEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learningen
titleIEEE Std 3127-2025num
typestandard
page40
treeIEEE - The Institute of Electrical and Electronics Engineers, Inc.:;2025
contenttypefulltext
subject keywordsfederated machine learning
subject keywordsFML
subject keywordsIEEE 3127™
subject keywordsblockchain
identifier DOI10.1109/IEEESTD.2025.10965995


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