A Method for Online Monitoring Data Release of Composite Submarine Cable Based on Horizontal Federated Learning

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI:10.12694/scpe.v24i3.2275

Xinli Lao, Jiajian Zhang, Chuanlian Gao, Huakun Deng, Yanlei Wei, Zhenzhong Liu

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引用次数: 0

Abstract

Conventional online composite submarine cable monitoring data release mostly adopts the method and principle of blockchain dynamic zoning consensus. In the data release process, there are omissions, and it takes a long time to complete the task, which reduces the timeliness of online composite submarine cable monitoring data release. Based on this, a new data publishing method is proposed by introducing horizontal federation learning. First, the online monitoring data of composite submarine cables are collected and preprocessed to eliminate the high-frequency capacitive effect of submarine cables. Secondly, manage composite submarine cable data nodes, transform the status relationship of data nodes, and ensure the quality of subsequent data release. A horizontal federation learning model is established to design the online monitoring data release process. The experimental results show that the new data release method is highly feasible. With the increasing online monitoring data of composite submarine cables, the time required for data release is short, and the timeliness is high.

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基于水平联邦学习的复合海缆在线监测数据发布方法

传统的在线复合海缆监测数据发布多采用区块链动态分区共识的方法和原理。数据发布过程中存在遗漏，完成任务耗时较长，降低了在线复合海缆监测数据发布的时效性。在此基础上，引入横向联邦学习，提出了一种新的数据发布方法。首先，采集复合海底电缆在线监测数据并进行预处理，消除海底电缆高频电容效应;其次，对复合海缆数据节点进行管理，转换数据节点的状态关系，保证后续数据发布的质量。建立了横向联邦学习模型，设计了在线监测数据发布流程。实验结果表明，新的数据发布方法是高度可行的。随着复合海底电缆在线监测数据的不断增加，数据发布所需时间短，及时性高。

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来源期刊

Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

2.00

自引率

0.00%

发文量

期刊介绍： The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.