Information Modeling Technique to Decipher Research Trends of Federated Learning in Healthcare

The aim of this study is to determine the most prevalent types of federated learning, discuss their uses in healthcare, highlight the most significant issues, and suggest methods for further research.

When it comes to handling distributed data, federated learning is revolutionary, especially in sensitive sectors like healthcare. In order to improve the outcomes of the growing number of healthcare studies, there must be a method to safely and effectively analyze and use this enormous data.

The purpose of this research is to use a large corpus of 6,800 healthcare studies published between 2000 and 2024 and apply topic modeling using Latent Semantic Analysis (LSA).

The corpus was analyzed using LSA with the goal of identifying latent themes that capture the spirit of federated learning in the healthcare industry. In order to provide an organized overview of the subject matter, a five-topic solution was devised. To guarantee relevance and clarity, the topics' coherence was assessed.

The term frequency and the inverse document frequency of high-loading terms provided five major topic solutions. The coherence score of the five-topic solution was achieved, i.e., 0.789, indicating a high level of relevance and integration among the identified topics. Different types of federated learning (FL), applications of FL, and the key challenges and the possible solution associated with FL have been analyzed.

This study highlights the significance of using FL to improve privacy-preserving data analysis in the healthcare field, which may lead to the development of creative solutions for complex problems.