Federated Learning and AI-Driven Edge Computing for Secure and Efficient Healthcare Data Processing
DOI:
https://doi.org/10.64137/XXXXXXXX/IJMST-V1I1P104Keywords:
Federated learning, Edge computing, Healthcare AI, Data privacy, Scalability, Model optimization, Real-time processing, Cross-modal learning, Ethical AI, Resource efficiencyAbstract
FL and AI-driven edge computing promise to boost both the safety, efficiency, and privacy of processing healthcare information. This work discusses how FL and edge computing come together to solve problems in healthcare, such as data security, performance, and immediate processing of information. Our report covers the current topics in research, outlines the major problems, and provides stories outlining how these technologies are applied in practice. Besides, we develop a plan for a good healthcare system design that depends on FL, edge computing, and analyze it by running simulations and working with real data. At the conclusion, the paper considers future studies and how these technologies will affect healthcare as a whole
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