Federated learning in edge computing: Enhancing data privacy and efficiency in resource-constrained environments
1 Independent Researcher, Bucharest, Romania.
2 Independent Researcher, Bucharest, Romania.
3 Independent Researcher, Bucharest, Romania.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(02), 205-214.
Article DOI: 10.30574/wjaets.2024.13.2.0563
Publication history:
Received on 07 October 2024; revised on 12 November 2024; accepted on 15 November 2024
Abstract:
Federated learning (FL) has become one of the most promising machine learning techniques that solve important data confidentiality and security challenges by training a model across decentralized devices without having raw data. Similarly, edge computing allows data analysis near the source, reducing time and using less bandwidth. This work examines the applications of federated learning in edge computing but focuses on scenarios with resource limitations, as seen with IoT devices and mobile networks. Reviewing the currently used approaches, issues, and trends, the features related to the future development involving opportunities and risks are considered. The emerging studies show that federated learning in the context of edge computing improves processing capacity in the federated model by preserving privacy concerns and is applicable in smart cities, healthcare smart grids, and self-driving systems. Some research directions for future works are suggested to address the scale-up and use of resources.
Keywords:
Federated Learning; Edge Computing; Data Privacy; Resource Efficiency; Machine Learning Optimization
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0