联合强度传播:在异质联合学习中共享对抗强度
Junyuan Hong1, Haotao Wang2, Zhangyang Wang2
1Department of Computer Science and Engineering Michigan State University.
概括
本研究介绍了一种联合学习 (FL) 策略,旨在从资源丰富的用户与资源有限的用户分享对抗性强度. 该方法增强了所有参与者的模型安全性,即使很少有用户进行对抗训练 (AT).
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) 允许在不共享原始数据的情况下进行协作模式培训.
- 由于非id数据和不同的计算资源,FL的异质用户面临挑战.
- 确保模型对抗对抗攻击的稳定性对于联合用户至关重要.
研究的目的:
- 制定一个联合学习策略,从资源丰富的用户传播对抗性强度到资源贫乏的用户.
- 为了应对应用对抗性培训 (AT) 在具有有限用户资源的联合环境中的挑战.
- 加强联合模型的安全性和可靠性,以防止恶意攻击.
主要方法:
- 提出了一种新的联合学习策略,用于传播对抗性强度.
- 引入了一种高效的传播方法,使用批量规范化来处理非iid数据.
- 在不同用户资源和数据分布的场景中评估方法的有效性.
主要成果:
- 证明现有的FL技术不足以在非iid用户中传播稳健性.
- 展示了拟议的基于批量规范化的强度传播方法的有效性.
- 在联合模型中实现了显著的对抗稳定性,即使只有少数用户能够负担得起对抗训练.
结论:
- 拟议的联合学习策略成功地向资源有限的用户传播了对抗性强度.
- 批量规范化技术是使异质联合环境中有效的稳定性转移成为可能的关键.
- 这种方法提高了联合模型的安全性,使它们更适应敌对攻击.
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