Edge-Intelligent Data Engineering: Federated Learning Architectures for IoT-Driven Data Pipelines

Authors

  • Surya Veera Brahmaji Rao Sunnam

Keywords:

IoT, Pipieline, Edge-Intelligent, Federatted Learning, Data Engineering

Abstract

In this paper, an edge-intelligent federated pipeline is evaluated to improve the latency, scalability, and reliability of the model of large IoTs. The quantitative experiments are used to test the proposed system against centralized and traditional federated architecture. The results show that the performance of models has greatly improved including a 59 percent latency reduction and a 79 percent network load reduction, faster model convergence, and a better gradient trust. The accuracy of detection of anomalies is also enhanced by the system and the system is also more resistant to adversarial updates. Scalability testing is necessary to guarantee the unchanged functionality with thousands of devices and less energy usage. Altogether, one can remark that the edge intelligence + federated coordination are more efficient, secure, and flexible data processing ecosystems.

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References

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Published

30.12.2024

How to Cite

Surya Veera Brahmaji Rao Sunnam. (2024). Edge-Intelligent Data Engineering: Federated Learning Architectures for IoT-Driven Data Pipelines. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4033–4040. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8022

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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