在物联网智能家居的去中心化联合学习中选择客户的激励意识的联合谈判方法
1School of Computing Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India. jaivinita.l@vit.ac.in.
Scientific reports
|October 2, 2025
概括
联合学习 (FL) 得到了激励意识联合谈判 (IAFB) 框架的增强,提高了公平性,准确性和效率,同时降低了物联网环境中 Man-in-the-Middle 攻击等安全风险.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 能够在分布式物联网设备上进行隐私保护培训.
- 现有的FL方法与客户端选择,数据异质性和安全漏洞,包括中间人攻击 (MITM) 斗争.
研究的目的:
- 引入激励意识联邦谈判 (IAFB) 框架,以应对关键的FL挑战.
- 为了提高物联网环境的FL的安全性,公平性和效率.
主要方法:
- 实现了纳什谈判,以实现最佳的客户选择.
- 利用基于Shapley价值的激励措施,以公平地分配奖励.
- 为了安全性和弹性,使用了分散的点对点 (P2P) 聚合和AES-GCM加密.
主要成果:
- IAFB提高了28%的参与公平性和6.5%的模型准确性.
- 收时间缩短了35%,通讯开销减少了39.5%.
- 该框架表现出对敌对威胁的增强弹性,并有效地减轻了MITM攻击.
结论:
- 在资源有限的物联网环境中,IAFB为安全和可扩展的联合学习提供了强大的解决方案.
- 拟议的框架在效率和安全方面显著改善了像FedAvg这样的传统FL方法.
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