Publication Date: 2024/03/04
Abstract: The integration of machine learning in edge computing has emerged as a transformative paradigm, offering unprecedented opportunities and challenges. This review paper explores the consequences for network architecture, privacy, security, and resource efficiency while also delving into the dynamic environment of this convergence. The article guides the reader through the developments in artificial intelligence (AI) in edge computing settings using current research findings. This article covers important topics such as energy use optimization and data processing efficiency, summarizing important discoveries and offering a comprehensive overview of the state of machine learning in edge computing. A thorough analysis of AI methods, compute offloading techniques, and security precautions clarifies the way forward for utilizing edge computing and machine learning in the future.
Keywords: Machine Learning, Edge Computing, Opportunities and Challenges, Architectural Layout, Security, Resource Constraints, Real-time Data Processing, Edge Computing Platforms, AI Algorithms, Data Privacy, Computing Offloading Strategies.
DOI: https://doi.org/10.5281/zenodo.10776717
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB1513.pdf
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