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IEEE 2986-2023 IEEE Recommended Practice for Privacy and Security for Federated Machine Learning

【国外标准】 IEEE Recommended Practice for Privacy and Security for Federated Machine Learning

本网站发布时间： 2025-10-27

IEEE 2986-2023
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适用范围：

This document provides recommended practices related to privacy and security for FML, including security and privacy principles, defense mechanisms against non-malicious failures and examples of adversarial attacks on a FML system. This document also defines an assessment framework to determine the effectiveness of a given defense mechanism under various settings. Data privacy and security are highly complex and increasingly regulated areas of law, and no recommended practice can provide… read more unconditional consistency with all applicable laws and regulations, which may also vary at the local, state and regional level. Users of this document should evaluate any implementation for considerations of data privacy, security and data ownership in the context of FML, and are responsible for conformance with all such laws and regulations. read less

基本信息

标准号：
IEEE 2986-2023
标准名称：
IEEE Recommended Practice for Privacy and Security for Federated Machine Learning
英文名称：
IEEE Recommended Practice for Privacy and Security for Federated Machine Learning
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57 页
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