联合学习的攻击面：分类法、网络防御、挑战和未来方向

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联合学习攻击面：分类法、网络防御、挑战和未来方向

人工智能评论第55卷,第 3569-3606页(2022)引用此文

摘要

在隐私保护限制的背景下，联合学习（FL）得到了大量的研究关注。通过联合训练深度学习模型，在受邀参与者的帮助下可以胜任各种训练任务。然而，FL涉及到大量涉及隐私和安全方面的攻击。本文展示了一个联合学习的工作流程，以及恶意客户端如何利用FL系统的漏洞来攻击系统。对现有的关于联合学习攻击面的分类法和分类的研究进行了系统的调查。与FL攻击面一样，攻击者损害了安全、隐私，获得了自由激励，并滥用了保密性、完整性和可用性（CIA）安全三要素。此外，阐述了针对FL攻击的最先进的防御方法，这有助于保护和减少攻击的可能性。解释了隐私攻击的FL模型和工具，以及它们的最佳方面和缺点。最后，讨论了技术挑战和可能的研究指导方针，作为建立强大的FL系统的未来工作。

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作者信息

所属机构

  1. 北京科技大学计算机与通信工程学院，北京，中国

    Attia Qammar & Huansheng Ning

  2. 瑞典Karlskrona，Blekinge技术学院计算机科学系

    丁建国

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  1. Attia Qammar

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  2. 丁建国

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  3. 宁环生

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Qammar, A., Ding, J. & Ning, H. Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions.Artif Intell Rev 55, 3569-3606 (2022). doi.org/10.1007/s10…

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关键词

  • 联合学习
  • 安全性
  • 隐私
  • 攻击面
  • 网络防御
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