SpyShield:一种Spyfall启发的防御机制,用于抵御联合学习中的毒害攻击
Youssef Elgharieb1, Wassim Alexan2, Minar El-Aasser3
1CSEN Department, Faculty of MET, German University in Cairo, Cairo, Egypt.
Scientific reports
|August 26, 2025
概括
通过使用客户端合作来检测中毒攻击,SpyShield提高了联合学习 (FL) 的安全性. 这种由Spyfall引发的新型防御机制在协作训练中优于现有的方法.
科学领域:
- 计算机科学
- 人工智能
- 网络安全
背景情况:
- 传统的机器学习 (ML) 中心架构带来数据隐私风险.
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模型培训,但仍然容易受到数据和模型中毒的攻击.
- 目前的FL防御通常依赖于服务器端的分析,缺乏客户端的合作.
研究的目的:
- 提出SpyShield,一个新的联邦学习防御机制, 利用客户合作识别恶意客户.
- 评估SpyShield对各种数据和模型中毒的强度和性能.
- 将SpyShield与不同攻击场景和强度的基准聚合算法进行比较.
主要方法:
- 开发了SpyShield,这是一款灵感来自社交游戏Spyfall的防御机制,利用客户端合作来检测恶意参与者.
- 评估了SpyShield在FashionMNIST数据集上的表现,与FedAvg,Krum,Multi-Krum,Median和Trimmed Mean聚合算法进行了对比.
- 在不同数量恶意客户端 (3/30,10/50,40/100) 的场景中对循环标签翻转,随机标签翻转和随机重量攻击进行了测试.
主要成果:
- 在所有测试的实验设置和攻击类型中,SpyShield表现出卓越的性能和弹性.
- 至少有一种SpyShield配置在每个场景中都比所有基准算法更高的准确性.
- 拟议的机制有效地识别了恶意客户,大大提高了联合学习中的模型安全性.
结论:
- 提供强大且可通用的防御机制来保护联合学习模型免受毒害攻击.
- SpyShield使用的客户合作策略超越了传统的集中式服务器端分析.
- 这些发现表明,在加强飞机安全和弹性方面,有合作战略的潜力.
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