相关实验视频
与GNN进行异步联合学习,以提高车辆互联网中的数据安全性
1Department of Information Technology and Cybersecurity, Shanghai Police College, Shanghai, China. qixiao0513@sina.com.
Scientific reports
|October 1, 2025
概括
本研究介绍了使用联合学习和图形神经网络的安全汽车互联网 (IoV) 框架. 它增强数据隐私,减少网络负载,并通过边缘计算和声誉系统提高效率.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 由于广泛的数据交换,汽车互联网 (IoV) 面临着重大的安全,隐私和数据完整性挑战.
- 当前的 IoV 系统在网络拥堵和高延迟方面扎,影响用户体验和数据可靠性.
研究的目的:
- 开发一个先进的框架来解决 IoV 生态系统中的安全性和效率问题.
- 为了提高数据隐私,完整性,并减少通信开销而不会影响数据质量.
主要方法:
- 将图形神经网络 (GNN) 与联邦学习集成,以实现安全的数据处理.
- 实施共识机制,利用边缘计算来减少延迟和提高可靠性.
- 引入声誉系统,以激励值得信赖的用户行为,并阻止恶意活动.
主要成果:
- 通过本地化处理和联合学习来确保车辆数据的隐私和完整性.
- 通过本地数据存储和高效的数据传输,减少网络拥堵和通信负载.
- 通过边缘驱动的共识机制,提高了数据交换可靠性,并将延迟降到最低.
结论:
- 拟议的框架为IoV生态系统提供了一个安全,高效和可扩展的解决方案.
- 像GNN,联合学习和边缘计算这样的先进技术对于克服IoV数据挑战至关重要.
- 声誉系统有效地促进诚实参与,提高网络的整体可信度.
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