安全和高效的联合学习对抗模型中毒攻击在横向和垂直数据分区
IEEE transactions on neural networks and learning systems
|November 5, 2024
概括
这项研究介绍了一种安全的混合联合学习 (FL) 方法来打击模型中毒攻击. 新方法降低了培训成本,提高了对复杂攻击的效率.
科学领域:
- 分布式系统 分布式系统
- 机器学习安全 机器学习安全
背景情况:
- 混合联合学习 (FL) 结合了水平和垂直的数据分区,用于增强的分布式系统.
- 混合FL容易受到模型中毒攻击,损害了全球模型的完整性.
- 由于数据多样性,现有的防御系统面临着高的检测成本和准确性问题.
研究的目的:
- 开发一个安全高效的混合FL框架来应对模型中毒攻击.
- 为了尽量减少培训成本和能源消耗,同时保持准确性.
- 为了应对在各种FL环境中检测恶意更新的挑战.
主要方法:
- 为分析定义了两个新的本地模型中毒攻击.
- 分析了混合FL的执行时间和能源消耗.
- 制定了一个通过马尔科夫决策过程和多代理强化学习 (MARL) 解决的优化问题.
- 提出了一种使用MARL.恶意设备检测 (MDD) 方法.
- 引入了基于模型变化一致性的中毒模型检测 (PMD) 方法.
主要成果:
- 该MDD方法减少了50%以上的培训成本,针对随机局部模型中毒攻击.
- 结合MDD和PMD方法在先进的自适应局部模型中毒 (ALMP) 攻击下保持了预期的准确性.
- 这两种方法都显示了执行时间和能源消耗的减少.
结论:
- 拟议的基于MARL的MDD和PMD方法提供了对混合FL模型中毒的强有力的防御.
- 该方法有效地平衡了分散式学习环境中的安全性,效率和准确性.
- 这项工作在确保混合联合学习系统方面取得了重大进展.
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