Intelligent Edge Computing for IoT Data Processing and AI Model Deployment
Author(s): Satyam Chauhan
Publication #: 2501036
Date of Publication: 10.07.2023
Country: USA
Pages: 1-16
Published In: Volume 9 Issue 4 July-2023
DOI: https://doi.org/10.5281/zenodo.14613685
Abstract
The exponential growth of IoT devices and the rising demand for AI-driven applications have introduced significant challenges in data processing, scalability, and latency. Intelligent Edge Computing (IEC) emerges as a transformative solution by processing data closer to its source, thus addressing these challenges while enhancing privacy and reducing bandwidth usage. This paper explores the architecture, techniques, and strategies of IEC for IoT data processing and AI model deployment. Key topics include edge architecture, lightweight AI algorithms, federated learning, and transfer learning for real-time decision-making. The study also evaluates its application in financial services, showcasing use cases in high-frequency trading, equity research, and fixed income analysis. Insights drawn emphasize the potential of IEC in shaping next-generation IoT ecosystems and its significance across multiple sectors, paving the way for a more distributed and efficient computational paradigm.
Keywords: AI Model Deployment, Edge Devices, Federated Learning, Federated Learning, IoT Data Processing.
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