This document provides recommended practices related to privacy and security for Federated Machine Learning, including security and privacy principles, defense mechanisms against non-malicious failures and examples of adversarial attacks on a Federated Machine Learning system. This document also defines an assessment framework to determine the effectiveness of a given defense mechanism under various settings.
- Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Joint Sponsors
-
C/LT
- Status
- Active PAR
- PAR Approval
- 2021-03-25
Working Group Details
- Society
- IEEE Computer Society
Learn More - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
SPFML-WG - Security and Privacy for Federated Machine Learning Working Group
Learn More - IEEE Program Manager
- Christy Bahn
Contact - Working Group Chair
- Zuping Wu
No Active Projects
No Active Standards
No Superseded Standards
No Inactive-Withdrawn Standards
No Inactive-Reserved Standards