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.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Joint Sponsors
-
C/LT
C/BDL
- Status
- Active PAR
- PAR Approval
- 2021-11-09
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
BFML - Blockchain-based Federated Machine Learning
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Ye Ouyang
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
No Active Projects
Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
No Active Standards
These standards have been replaced with a revised version of the standard, or by a compilation of the original active standard and all its existing amendments, corrigenda, and errata.
No Superseded Standards
These standards have been removed from active status through a ballot where the standard is made inactive as a consensus decision of a balloting group.
No Inactive-Withdrawn Standards
These standards are removed from active status through an administrative process for standards that have not undergone a revision process within 10 years.
No Inactive-Reserved Standards