At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.

The emergence of large language models (LLMs), like ChatGPT, has caused a major shift in all academic and professional fields; education being the one impacted the most deeply. The first 200 most cited articles from Scopus on the topic of "large language models" and "education" were selected for a scientometric analysis of this survey. The aim is to map the intellectual landscape, specifying dominant research themes, key contributors, methodological trends, and the emergent challenges that researchers faced during this initial, explosive phase of research. This review systematically analyzes the publication trends, authorship patterns, geographical and institutional contributions, and the conceptual structure of the literature to provide a quantitative and qualitative snapshot of a field that is rapidly changing. The analysis uncovers a domain where exploratory studies, conceptual debates, and early empirical validations dominate, and where the focus of research is mainly on higher education and medical training. The major issues being discussed are academic integrity, assessment redesign, and the ethical integration of AI, while promising applications are personalized learning, automated feedback, and content generation. This survey is a reference for a better understanding of the roots and forefronts of LLM-related educational research as it passes from the initial reaction to systematic integration.

The introduction of Large Language Models (LLMs) in the healthcare sector is a major step, which can for sure transform all aspects of medicine including practice, research, and education. This review of publications presents the first 200 most cited articles which were obtained from a Scopus search on the terms "large language models" and "healthcare". The purpose was to elucidate the knowledge and trends in this fast-growing area. The analysis looks at the yearly publications, the main journals, the most prominent authors and institutions, the leading research areas, the methodological approaches used, and the ethical and regulatory issues that are the most talked about. The results show an increase in the number of scholars interested in this field, especially in 2023 and 2024. Besides that, it was found that there is a lot of high-impact publications going on in the leading multidisciplinary and specialized medical journals areas. The largest research areas that were found are: clinical trials and their outcomes, ethical and governance frameworks, educational integration, and technological advancements and surveys. The research is mainly focused on testing the performance of LLMs like ChatGPT in particular medical tasks but in the background, there are profound concerns about their accuracy, bias, and safety. This review presents the current knowledge state, points out the most active and leading research fronts, and recognizes the gaps with future directions hence it offers the most basic reference for researchers, doctors, and policymakers who will be dealing with the LLMs incorporation into the healthcare system.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.