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IEEE Recommended Practice for Privacy and Security for Federated Machine Learning
IEEE Std 2986-2023
| contributor author | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| date accessioned | 2024-12-17T08:47:25Z | |
| date available | 2024-12-17T08:47:25Z | |
| date copyright | 26 April 2024 | |
| date issued | 2024 | |
| identifier other | 10507779.pdf | |
| identifier uri | http://yse.yabesh.ir/std;query=author:%22NAVY%20-%20YD%20-%20Naval%20Facilities%20Engineering%20Command%22/handle/yse/336474 | |
| description abstract | 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. | |
| language | English | |
| publisher | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| title | IEEE Recommended Practice for Privacy and Security for Federated Machine Learning | en |
| title | IEEE Std 2986-2023 | num |
| type | standard | |
| page | 57 | |
| tree | IEEE - The Institute of Electrical and Electronics Engineers, Inc.:;2024 | |
| contenttype | fulltext | |
| subject keywords | security | |
| subject keywords | IEEE 2986™ | |
| subject keywords | machine learning | |
| subject keywords | federated machine learning | |
| subject keywords | FML | |
| subject keywords | privacy | |
| identifier DOI | 10.1109/IEEESTD.2024.10507779 |
