在被动光学网络中进行动态带宽切割,以实现联合学习
Alaelddin F Y Mohammed1, Joohyung Lee2, Sangdon Park3
1Information Technology, Department of International Studies, Dongshin University, 67, Dongshindae-gil, Naju-si 58245, Republic of Korea.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种新的动态带宽分配 (DBA) 方法,用于通过被动光学网络 (PON) 传输联合学习 (FL) 流量. 新方法有效地管理带宽,显著减少6G网络上游延迟.
科学领域:
- 电信工程 电信工程 电信工程
- 机器学习 机器学习
- 网络管理 网络管理
背景情况:
- 联合学习 (FL) 通过在设备上本地训练模型来提供分散的机器学习.
- 被动光学网络 (PON) 是高速通信的关键基础设施.
- 将FL与PON集成为6G提供了机会,但需要对FL流量进行高效的带宽管理.
研究的目的:
- 为6G环境探索联合学习 (FL) 与被动光学网络 (PON) 的集成.
- 为应对PON内部复杂的FL流量带宽管理的挑战.
- 引入和评估一种新的动态带宽分配 (DBA) 方法,用于PON的FL流量.
主要方法:
- 开发了一种针对联合学习 (FL) 流量的新型动态带宽分配 (DBA) 算法.
- 在PON框架内模拟了拟议的DBA方法,以分析其性能.
- 使用关键网络性能指标,将新的DBA方法与最先进的解决方案进行了比较.
主要成果:
- 拟议的DBA方法有效地分配PON带宽用于FL流量生成.
- 证明了在PON中使用多个上游拨款分配用于FL流的好处.
- 与现有解决方案相比,模拟显示出更高的性能,特别是在减少上游延迟方面.
结论:
- 新的DBA方法有效地提高了在PON中FL流量的带宽管理.
- 在高效的带宽分配的支持下,FL和PON的集成是6G服务的一个有希望的推动因素.
- 这项研究为未来6G网络至关重要的实时,数据密集型服务铺平了道路.
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