Edge-to-Cloud Architectures for Machine Learning-Driven Data Analytics
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P101Keywords:
Edge computing, Cloud computing, Machine learning, Data analytics, Task offloading, Federated learning, Continuous learning, Privacy, Scalability, real-time applicationsAbstract
The integration of edge computing and cloud computing has emerged as a powerful paradigm for handling the increasing volume, velocity, and variety of data generated by modern applications. This paper explores the design and implementation of edge-to-cloud architectures specifically tailored for machine learning (ML)-driven data analytics. We begin by providing an overview of the challenges and opportunities in this domain, followed by a detailed discussion of the architectural components, including data preprocessing, model training, inference, and continuous learning. We then present a case study and experimental results to evaluate the performance and efficiency of the proposed architecture. Finally, we discuss the implications of our findings and suggest future research directions
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References
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