Cookies
We need your consent to use the individual data so that you can see information about your interests, among other things. Click "OK" to give your consent.
IEEE P3127
IEEE Approved Draft Guide for an Architectural Framework for Blockchain-based Federated Machine Learning
Translate name
STANDARD published on 12.3.2025
| Availability | Sold out |
| Price | ONREQUEST excl. VAT |
| ON REQUEST |
The information about the standard:
Designation standards: IEEE P3127
Publication date standards: 12.3.2025
SKU: NS-1214989
Approximate weight : 300 g (0.66 lbs)
Country: International technical standard
Annotation of standard text IEEE P3127 :
New IEEE Standard - Active - Draft.
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 use of data, withdrawing use of data, and potentially sell data under specified conditions.
ISBN: 979-8-8557-1329-9, 979-8-8557-1329-9
Number of Pages: 38
Product Code: STDUD27389, STDAPE27389
Keywords: blockchain, federated machine learning, FML, IEEE 3127™
Category: 319
Draft Number: P3127/D0.7, Jun 2024 - UNAPPROVED DRAFT, P3127/D0.7, Jun 2024 - APPROVED DRAFT
We recommend:
Technical standards updating
Do you want to make sure you use only the valid technical standards?
We can offer you a solution which will provide you a monthly overview concerning the updating of standards which you use.
Would you like to know more? Look at this page.
