A Bibliometric Analysis on Federated Learning
With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. The emergence of FL as a novel machine-learning approach and the volume of relevant papers and studies now call for a thorough...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-12-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/4237589 |
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| author | Ersin Namlı Yusuf Sait Türkan Mesut Ulu Ömer Algorabi |
| author_facet | Ersin Namlı Yusuf Sait Türkan Mesut Ulu Ömer Algorabi |
| author_sort | Ersin Namlı |
| collection | DOAJ |
| description | With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. The emergence of FL as a novel machine-learning approach and the volume of relevant papers and studies now call for a thorough investigation of FL. In the present research, an analysis was conducted on 3107 articles about federated learning exported from the Web of Science (WoS). The paper performs a bibliometric analysis to examine the productivity, citations, and bibliographic matching of significant authors, universities/institutions, and countries. The evolution of research material on federated learning over time was analyzed in the research. The study also provides comprehensive analysis by examining the most frequently used terms in the articles and attempting to identify trending areas of study with federated learning. This paper offers primary information on FL for readers worldwide and a comprehensive and accurate analysis of potential contributors. |
| format | Article |
| id | doaj-art-49ca31a8b3a9416daa7163ac35020e9d |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-49ca31a8b3a9416daa7163ac35020e9d2025-02-05T18:13:02ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-12-0110487589810.28979/jarnas.1555351453A Bibliometric Analysis on Federated LearningErsin Namlı0https://orcid.org/0000-0001-5980-9152Yusuf Sait Türkan1https://orcid.org/0000-0001-7240-183XMesut Ulu2https://orcid.org/0000-0002-5591-8674Ömer Algorabi3https://orcid.org/0000-0002-2016-8674İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞAİSTANBUL ÜNİVERSİTESİ-CERRAHPAŞABANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİİSTANBUL ÜNİVERSİTESİ-CERRAHPAŞAWith the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. The emergence of FL as a novel machine-learning approach and the volume of relevant papers and studies now call for a thorough investigation of FL. In the present research, an analysis was conducted on 3107 articles about federated learning exported from the Web of Science (WoS). The paper performs a bibliometric analysis to examine the productivity, citations, and bibliographic matching of significant authors, universities/institutions, and countries. The evolution of research material on federated learning over time was analyzed in the research. The study also provides comprehensive analysis by examining the most frequently used terms in the articles and attempting to identify trending areas of study with federated learning. This paper offers primary information on FL for readers worldwide and a comprehensive and accurate analysis of potential contributors.https://dergipark.org.tr/en/download/article-file/4237589federated learningbibliometric analysisnetwork analysis |
| spellingShingle | Ersin Namlı Yusuf Sait Türkan Mesut Ulu Ömer Algorabi A Bibliometric Analysis on Federated Learning Journal of Advanced Research in Natural and Applied Sciences federated learning bibliometric analysis network analysis |
| title | A Bibliometric Analysis on Federated Learning |
| title_full | A Bibliometric Analysis on Federated Learning |
| title_fullStr | A Bibliometric Analysis on Federated Learning |
| title_full_unstemmed | A Bibliometric Analysis on Federated Learning |
| title_short | A Bibliometric Analysis on Federated Learning |
| title_sort | bibliometric analysis on federated learning |
| topic | federated learning bibliometric analysis network analysis |
| url | https://dergipark.org.tr/en/download/article-file/4237589 |
| work_keys_str_mv | AT ersinnamlı abibliometricanalysisonfederatedlearning AT yusufsaitturkan abibliometricanalysisonfederatedlearning AT mesutulu abibliometricanalysisonfederatedlearning AT omeralgorabi abibliometricanalysisonfederatedlearning AT ersinnamlı bibliometricanalysisonfederatedlearning AT yusufsaitturkan bibliometricanalysisonfederatedlearning AT mesutulu bibliometricanalysisonfederatedlearning AT omeralgorabi bibliometricanalysisonfederatedlearning |