6G网络中毫米波频谱的联合学习:应用,挑战,前进方向和开放研究问题
Faizan Qamar1, Syed Hussain Ali Kazmi1, Maraj Uddin Ahmed Siddiqui2
1Center of Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
PeerJ. Computer science
|December 9, 2024
概括
联合学习通过实现分散的培训来应对6G毫米波挑战. 一种名为FEADSBO的新方法优化了带宽和能源,以实现安全,高效的6G网络.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 6G网络承诺增强连接性,依靠毫米波 (mmWave) 频谱.
- 毫米波的独特传播和安全挑战阻碍了6G实现.
- 集中式深度学习 (DL) /机器学习 (ML) 模型面临数据隐私和集中化问题.
研究的目的:
- 在6G毫米波网络中探索联合学习 (FL) 应用.
- 确定适应6G毫米波通信FL的挑战.
- 为高效和安全的支持FL的6G毫米波系统提出一种新的解决方案.
主要方法:
- 对支持FL的6G网络和毫米波通信集成的全面审查.
- 分析包括带宽,功耗和同步在内的挑战.
- 关于联合能源意识动态同步与带宽优化 (FEADSBO) 的建议.
主要成果:
- 在6G毫米波中确定FL的关键应用.
- 详细讨论带宽,功率和同步挑战.
- 引入FEADSBO作为缓解这些挑战的解决方案.
结论:
- 在6G中,FL提供了一种分散的方法来克服DL/ML限制.
- FEADSBO为高效和安全的支持FL的6G毫米波系统提供了路线图.
- FL和mmWave之间的协同作用对于未来的6G网络演变至关重要.
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