This standard provides a common framework for deploying federated machine learning within trusted data space. It defines a reference architecture as well as functional and non-functional requirements that enable interoperable and privacy-preserving data collaboration across organizations. Federated machine learning and trusted data spaces enable the standard's basic functions through their intrinsic capabilities. The standard also specifies essential non-functional requirements, most notably those concerning privacy and security requirements for this converged application.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2026-09-25
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
FLDS - Federated Machine Learning Application in Trusted Data Space
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Cuiting HUANG
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