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一个混合联合学习框架与生成人工智能用于保护隐私和可持续的安全性在物联网支持的智能环境中
Venkadeshan Ramalingam1, Basant Kumar2, Shashi Kant Gupta3,4
1Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia. pdf.Venkadeshan@lincoln.edu.my.
Scientific reports
|January 22, 2026
概括
本研究引入了一种结合混合联合学习和生成人工智能 (GenAI) 的新框架,以提高物联网 (IoT) 设备的安全性和可持续性. 这种方法确保了数据隐私和能源效率,以确保安全,环保的物联网生态系统.
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:
- 在智能生态系统中物联网 (IoT) 设备的扩散带来了重大的安全和隐私挑战.
- 网络连接的增加,虽然使数据驱动的决策成为可能,但暴露了敏感信息并提高了安全风险.
研究的目的:
- 为安全和可持续的物联网网络和设备提出一个新的框架.
- 整合混合联合学习 (HFL) 和生成人工智能 (GenAI) 以提高物联网安全性和性能.
- 解决隐私问题,提高物联网系统的能源效率.
主要方法:
- 实施混合联合学习框架,以实现安全的设备学习.
- 利用GenAI进行信息增强和异常检测.
- 整合差异性隐私技术和区块链,以加强数据保护和模型验证.
- 能源效率模型优化和边缘智能的应用,以实现可持续性.
主要成果:
- 为物联网开发一种新的联合生成架构的开发.
- 证明加强了网络威胁的抵御力和减少了通讯开销.
- 通过先进的隐私保护技术,确保用户数据的保密性.
- 通过模拟和实验室测试在建筑和能源管理中验证框架.
结论:
- 拟议的框架为物联网环境提供了一个安全,可持续和保护隐私的解决方案.
- 这项研究推进了可持续人工智能的最新技术,以实现安全和环保的物联网.
- 混合HFL和GenAI模型为未来的物联网安全性和效率提供了强大的架构.
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