IEEE P2986

$42.25

IEEE Draft Recommended Practice for Privacy and Security for Federated Machine Learning

Published By	Publication Date	Number of Pages
IEEE	N/A	61

Guaranteed Safe Checkout

Category: IEEE

If you have any questions, feel free to reach out to our online customer service team by clicking on the bottom right corner. We’re here to assist you 24/7.
Email:[email protected]

Description

New IEEE Standard – Active – Draft. 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.

Additional information

Standard Title	IEEE Draft Recommended Practice for Privacy and Security for Federated Machine Learning
Published Code	IEEE
Publication Date	N/A
Pages Count	61

Preview PDF


PDF Format	Searchable	Printable	Preview Now

IEEE P2986

Related products