强大的基于学习的联邦分类器,用于智能电网电力质量障碍
Maazen Alsabaan1, Abdelrhman Elsayed2, Atef Bondok3
1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
联合学习 (FL) 使智能电网运营商能够在不共享敏感数据的情况下协同训练电源质量干扰 (PQD) 检测模型. 一个新的防御机制保护这些FL模型免受数据中毒攻击,增强电网安全.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 智能电网需要先进的方法来检测电力质量干扰 (PQD).
- 由于可再生能源和非线性负载增加的PQD需要强大的检测.
- 数据隐私问题限制了用于PQD分类的集中深度学习的使用.
研究的目的:
- 开发和评估基于联邦学习 (FL) 的PQD检测分类器.
- 在PQD分类中评估FL模型对数据中毒攻击的脆弱性.
- 实施和验证FL对PQDs的中毒攻击的防御机制.
主要方法:
- 开发基于FL的PQD检测分类器,并将其与集中模型进行比较.
- 模拟了五个数据中毒攻击场景,以评估模型的稳定性.
- 实现了一个检测机制来识别和隔离恶意客户端更新.
主要成果:
- 与集中式模型相比,FL模型的性能略有下降 (97%至96%的准确性).
- 数据中毒攻击显著降低了分类器的准确性.
- 实施的防御机制有效地缓解了受毒更新影响的性能下降.
结论:
- FL为智能电网中的PQD分类提供了一种保护隐私的方法.
- FL模型易受数据中毒攻击的影响,影响分类准确性.
- 拟议的防御机制增强了基于FL的PQD分类器对抗对方攻击的稳定性.
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