The Convergence of Blockchain and Federated Learning: A Bibliometric Exploration of Decentralized AI Yuhefizar, Ronal Watrianthos
Politeknik Negeri Padang, Padang, Indonesia
Abstract
The convergence of blockchain and federated learning has emerged as a transformative paradigm in decentralized artificial intelligence and secure data sharing. This study presents a comprehensive bibliometric analysis of research at the intersection of these technologies from 2018 to 2024, addressing the need for a holistic understanding of this rapidly evolving field. Utilizing data from the Web of Science Core Collection, we employed advanced bibliometric tools and ^Keyword Plus^ analysis to identify key research clusters and highlight emerging trends. Our findings reveal an extraordinary annual growth rate of 99.48% in publications, with 556 documents from 253 sources identified. The field demonstrates high collaboration, evidenced by an average of 4.73 co-authors per document and 44.06% international co-authorship. China emerges as the dominant force, contributing 48.7% of total publications. Analysis of influential papers underscores a consistent focus on privacy preservation, security enhancement, and application in domains such as Industrial IoT and vehicular networks. Emerging trends include the integration with edge computing, optimization of resource allocation, and applications in smart transportation and healthcare. This study contributes a comprehensive bibliometric analysis on blockchain and federated learning convergence highlighting the potential synergies of these technologies in addressing critical challenges in data privacy and collaborative AI, as well as identifying key areas for future research and development.
