A Bibliometric Analysis on Federated Learning

Year 2024, Volume: 10 Issue: 4, 875 - 898, 31.12.2024

Ömer Algorabi , Yusuf Sait Türkan , Mesut Ulu , Ersin Namlı

https://doi.org/10.28979/jarnas.1555351

Abstract

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.

Keywords

Federated learning, bibliometric analysis, network analysis

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