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Recommended Practice for Privacy and Security for Federated Machine Learning

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
Active PAR
PAR Approval

Working Group Details

IEEE Computer Society
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Sponsor Committee
C/AISC - Artificial Intelligence Standards Committee
Working Group
SPFML-WG - Security and Privacy for Federated Machine Learning Working Group
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IEEE Program Manager
Christy Bahn
Working Group Chair
Zuping Wu
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