联合学习生命周期中的威胁和防御:全面的调查和挑战
概括
联合学习 (FL) 通过在本地训练模型来增强机器学习中的隐私. 本综述调查了FL在其整个生命周期中的威胁和防御,旨在获得可靠的AI.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 通过在本地训练模型和汇总更新来实现保护隐私的分布式机器学习 (ML),防止原始数据的共享.
- 虽然FL可以缓解直接的隐私泄露,但其分布式性质会在FL服务生命周期中引入漏洞.
- 这些漏洞可能会降低模型的实用性或损害参与者的隐私.
研究的目的:
- 在联合学习中系统地审查最先进的威胁和防御框架.
- 分析FL威胁与现有防御之间的关系,包括它们的权衡.
- 在现实世界应用中评估FL防御战略的实用性.
主要方法:
- 关于代表性和最先进的FL威胁和防御机制的全面文献综述.
- 根据威胁对实用性和隐私的影响对威胁进行分类.
- 分析国防框架的有效性,权衡和现实世界的适用性.
主要成果:
- 识别和分类了各种影响模型实用性和隐私的FL威胁.
- 审查和分析了许多旨在减轻这些威胁的防御框架.
- 评估了不同防御策略的实际适用性和权衡.
结论:
- 现有的防御框架显示出希望,但需要进一步评估,以便在现实世界中部署.
- 在可信的联合学习中研究瓶需要解决.
- 未来的方向应侧重于为整个FL生命周期开发强大而实用的防御.
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