Multiagent DDPG-Based Deep Learning for Smart Ocean Federated Learning IoT Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Dohyun | - |
dc.contributor.author | Jeon, Joohyung | - |
dc.contributor.author | Park, Soohyun | - |
dc.contributor.author | Kim, Joongheon | - |
dc.contributor.author | Cho, Sungrae | - |
dc.date.accessioned | 2021-08-30T12:54:25Z | - |
dc.date.available | 2021-08-30T12:54:25Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/52592 | - |
dc.description.abstract | This article proposes a novel multiagent deep reinforcement learning-based algorithm which can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) devices in the ocean environment. According to the fact that underwater networks are relatively not easy to set up reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, FL-based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters that are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource allocation (JCARA) method is required and it is designed inspired by multiagent deep deterministic policy gradient (MADDPG) to deal with distributed situations and unexpected time-varying states. The performance evaluation results show that our proposed MADDPG-based algorithm achieves 80% and 41% performance improvements than the standard actor-critic and DDPG, respectively, in terms of the downlink throughput. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | OPTIMIZATION | - |
dc.title | Multiagent DDPG-Based Deep Learning for Smart Ocean Federated Learning IoT Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Joongheon | - |
dc.identifier.doi | 10.1109/JIOT.2020.2988033 | - |
dc.identifier.scopusid | 2-s2.0-85092707516 | - |
dc.identifier.wosid | 000577624800055 | - |
dc.identifier.bibliographicCitation | IEEE INTERNET OF THINGS JOURNAL, v.7, no.10, pp.9895 - 9903 | - |
dc.relation.isPartOf | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.title | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.volume | 7 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 9895 | - |
dc.citation.endPage | 9903 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Wireless communication | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Resource management | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Oceans | - |
dc.subject.keywordAuthor | Adaptation models | - |
dc.subject.keywordAuthor | Deep reinforcement learning | - |
dc.subject.keywordAuthor | federated learning (FL) | - |
dc.subject.keywordAuthor | smart ocean networks | - |
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