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    • P2986/D1.1, Sept 2023 - APPROVED DRAFT
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Full Description

Scope

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.

Purpose

The purpose of this recommended practice is to provide a resource on the topics of security and privacy for designers and users of Federated Machine Learning systems and to accelerate the deployment of Federated Machine Learning technology across industries.

Abstract

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.