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IEEE 2986-2023
IEEE Recommended Practice for Privacy and Security for Federated Machine Learning
Translate name
STANDARD published on 26.4.2024
The information about the standard:
Designation standards: IEEE 2986-2023
Publication date standards: 26.4.2024
SKU: NS-1182462
The number of pages: 57
Approximate weight : 171 g (0.38 lbs)
Country: International technical standard
Category: Technical standards IEEE
Annotation of standard text IEEE 2986-2023 :
New IEEE Standard - Active.
Privacy and security issues pose great challenges to the federated machine leaning (FML) community. A general view on privacy and security risks while meeting applicable privacy and security requirements in FML is provided. This recommended practice is provided in four parts: malicious failure and non-malicious failure in FML, privacy and security requirements from the perspective of system and FML participants, defensive methods and fault recovery methods, and the privacy and security risks evaluation. It also provides some guidance for typical FML scenarios in different industry areas, which can facilitate practitioners to use FML in a better way.
ISBN: 979-8-8557-0705-2, 979-8-8557-0706-9
Number of Pages: 57
Product Code: STD26928, STDPD26928
Keywords: federated machine learning, FML, IEEE 2986™, machine learning, privacy, security
Category: Computer Security and Privacy
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