An Intelligent secure framework for Edge in Smart health care datasets using Federated AI Models

Abstract

The integration of edge computing with federated AI models has transformed the landscape of smart healthcare, offering enhanced data security and real-time processing capabilities. Intentions of a comprehensive literature review is to explore the advancements and challenges in developing intelligent and secure frameworks for edge computing in smart healthcare datasets. By analyzing 24 relevant studies published between 2019 and 2024, the research focuses on the implementation of federated AI models to ensure data privacy, reduce latency, and amplify the overall efficacy of healthcare systems. The review embraces the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to provide a extensive analysis of existing frameworks, identifying key trends, technological innovations, and potential vulnerabilities. The findings reveal significant progress in leveraging edge computing for secure and intelligent healthcare solutions, highlighting the critical role of federated AI in enabling decentralized data processing without compromising patient privacy. The study emphasizes the need for improved frameworks to address challenges in scalability, interoperability, and the evolving nature of healthcare data. It also identifies gaps in standardizing these frameworks across diverse healthcare applications, posing barriers to broader adoption. This review offers key insights and suggests future research to enhance the security, elasticity, and versatility of intelligent peripheral processing in smart healthcare.