DSPFL:一个深层的标志共享个性化联合学习计划,以缓解中毒攻击
IEEE transactions on neural networks and learning systems
|August 27, 2025
概括
这项研究引入了一个新的工业物联网 (IIoT) 安全的联合学习 (FL) 计划. 深层标志共享个性化FL (DSPFL) 方法提高了异常检测的准确性.
科学领域:
- 网络安全
- 机器学习
- 工业物联网 (IIoT)
背景情况:
- 机器学习 (ML) 和联合学习 (FL) 对于工业物联网 (IIoT) 安全至关重要.
- 毒性攻击威胁FL模型,特别是IIoT中的非独立和相同分布的 (非IID) 数据.
- 由于数据异质性,区分恶意局部模型是具有挑战性的.
研究的目的:
- 提出一个强大的联合学习方案,能够抵御IIoT环境中的毒害攻击.
- 提高IIoT个性化异常检测模型的准确性和稳定性.
- 解决非ID数据和对抗性操纵在FL所带来的挑战.
主要方法:
- 引入深层标志共享个性化FL (DSPFL) 方案.
- 来自局部模型深层的随机梯度标志 (SignSGD) 的聚合.
- 在本地保留浅层模型以实现个性化和隐私.
主要成果:
- DSPFL表现出强大的抗毒性.
- 该方案提高了个性化的本地模型的准确性和稳定性.
- 实验结果显示,与现有方法相比,个性化模型的整体准确性高出20%.
结论:
- DSPFL有效地缓解了针对IIoT的个性化FL中毒攻击.
- 拟议的方法为智能工业中安全和准确的异常检测提供了有希望的解决方案.
- DSPFL在竞争条件下平衡全球模型的稳定性与本地模型的个性化.
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