最佳定向非循环图为节能物联网通信网络的联合学习模型.
G Nalinipriya1, E Laxmi Lydia2, S Rama Sree3
1Department of Information Technology, Saveetha Engineering College, Chennai, Tamilnadu, 602 105, India.
Scientific reports
|September 28, 2024
概括
本研究介绍了物联网网络的新联合学习 (FL) 方法,优化设备寿命和能源效率. LM-ODAGFL技术使用定向环形图 (DAG) 和阿基米德优化算法 (AOA) 来减少能源消耗和训练损失.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 无线通信无线通信
背景情况:
- 联合学习 (FL) 允许在设备上进行分布式计算,以提高效率.
- 使用FL管理大规模,资源有限的物联网网络带来了重大挑战.
- 设备异步和高能耗是当前FL应用中的关键问题.
研究的目的:
- 为节能物联网网络引入一种新的技术,即使用最佳定向环形图联合学习 (LM-ODAGFL) 实现终身最大化.
- 为了解决设备异步并最大限度地减少FL中的资源使用,使用定向环形图 (DAG) 模型.
- 通过阿基米德的优化算法 (AOA) 来优化FL模型并减少用户的能源消耗.
主要方法:
- LM-ODAGFL技术将FL与元启发式优化算法集成在一起.
- 用一个定向环形图 (DAG) 模型来管理FL框架内的设备异步.
- 使用阿基米德的优化算法 (AOA) 来优化DAG模型,重点是减少能源和最大限度地减少训练损失.
主要成果:
- 与SDAGFL和ESDAGFL相比,LM-ODAGFL技术的能量消耗显著降低.
- 在FMNIST-Clustered数据集中,LM-ODAGFL每轮的能量消耗在0.373到0.485千兆日之间.
- 在Poets数据集中,LM-ODAGFL每轮消耗了16.27至20.34千兆日,超过了现有方法.
结论:
- 拟议的LM-ODAGFL技术为物联网通信网络中的联合学习提供了优质,节能的解决方案.
- DAG和AOA的集成有效地解决了设备异步问题,并优化了资源利用.
- 实验结果验证了LM-ODAGFL在节能和模型效率方面提高的性能.
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