FGC: GCN-Based Federated Learning Approach for Trust Industrial Service Recommendation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yin, Yuyu | - |
dc.contributor.author | Li, Youhuizi | - |
dc.contributor.author | Gao, Honghao | - |
dc.contributor.author | Liang, Tingting | - |
dc.contributor.author | Pan, Qianqian | - |
dc.date.accessioned | 2023-05-17T00:42:14Z | - |
dc.date.available | 2023-05-17T00:42:14Z | - |
dc.date.created | 2023-05-15 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87797 | - |
dc.description.abstract | With the development of the Industrial Internet of Things system, the huge amount of devices, services, and continuous data, making it difficult to discover a trusted service in complex scenarios. To better leverage knowledge and historical behavior, recommendation systems are applied. However, the model accuracy closely depends on training data size; there is a great risk of data leaking by collecting from multiple departments. To solve these problems, we propose a graph-convolutional-neural-network-based federated approach, which accurately recommends proper service for participating clients without gathering the raw data. Specifically, each client trains locally and uploads the weights of their model to the server for aggregation. Besides, the potential overlapping services of different clients are leveraged to guide the embedding aggregation and sharing, which, in turn, optimize the local training results. Their sensitive scenarios' embedding is kept locally. Owing to the model aggregation, it also resists the poisoning attack to some degree. In addition, the comprehensive experiments on classic public recommendation datasets evaluate the feasibility, effectiveness, trustworthiness, and potential influences. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.title | FGC: GCN-Based Federated Learning Approach for Trust Industrial Service Recommendation | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000966804900001 | - |
dc.identifier.doi | 10.1109/TII.2022.3214308 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.19, no.3, pp.3240 - 3250 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85140747434 | - |
dc.citation.endPage | 3250 | - |
dc.citation.startPage | 3240 | - |
dc.citation.title | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Gao, Honghao | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Industrial Internet of Things | - |
dc.subject.keywordAuthor | Data privacy | - |
dc.subject.keywordAuthor | Federated learning | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Servers | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Informatics | - |
dc.subject.keywordAuthor | federated learning (FL) | - |
dc.subject.keywordAuthor | graph convolutional neural network (GCN) | - |
dc.subject.keywordAuthor | trust industrial service recommendation | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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