Privacy-Preserving Learning Models for Communication: A tutorial on Advanced Split Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tran, N.-P. | - |
dc.contributor.author | Dao, N.-N. | - |
dc.contributor.author | Nguyen, T.-V. | - |
dc.contributor.author | Cho, Sungrae | - |
dc.date.accessioned | 2022-12-28T02:42:18Z | - |
dc.date.available | 2022-12-28T02:42:18Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59780 | - |
dc.description.abstract | Along with the development of the internet, there is an explosion of data that is assembled from diverse sources such as mobile phones or sensors with unprecedented volumes. Big data paves the way for devising state-of-the-art Machine Learning models, which are employed for various tasks such as predicting or analysis, and bring massive impacts on people's daily lives. However, traditional learning architectures pose risks of privacy leakage. In deal with this problem, Split Learning was introduced. Split Learning is an emerging Distributed Collaborative Machine Learning approach with privacy-preserving nature. In this work, we first introduce the fundamentals of Split Learning and then describe major challenges of current learning models. We then depict modern Split Learning and their potential applications in variety fields. © 2022 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Privacy-Preserving Learning Models for Communication: A tutorial on Advanced Split Learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICTC55196.2022.9952628 | - |
dc.identifier.bibliographicCitation | International Conference on ICT Convergence, v.2022-October, pp 1059 - 1064 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85143255763 | - |
dc.citation.endPage | 1064 | - |
dc.citation.startPage | 1059 | - |
dc.citation.title | International Conference on ICT Convergence | - |
dc.citation.volume | 2022-October | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Distributed Collaborative Machine Learning | - |
dc.subject.keywordAuthor | Federated Learning | - |
dc.subject.keywordAuthor | privacy-preserving | - |
dc.subject.keywordAuthor | Split Learning | - |
dc.description.journalRegisteredClass | scopus | - |
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