Privacy and security issues pose great challenges to the federated machine leaning community. A general view on privacy and security risks while meeting applicable privacy and security requirements in federated machine learning is provided. A recommended practice is provided in four parts: malicious failure and non-malicious failure in federated machine learning, privacy and security requirements from the perspective of system and federated machine learning participants, defensive methods and fault recovery methods and the privacy and security risks evaluation. It also provides some guidance for typical federated learning scenarios in different industry areas which can facilitate practitioners to use federal learning in a better way.
- 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 About IEEE Computer Society - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
SPFML-WG - Security and Privacy for Federated Machine Learning Working Group
Learn More About SPFML-WG - Security and Privacy for Federated Machine Learning Working Group - IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Zuping Wu
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