Enhancing Edge Computing Performance for IoT Applications Using Federated Learning Techniques
DOI:
https://doi.org/10.62951/ijcts.v1i1.57Keywords:
Edge computing, federated learning, IoT, data privacy, latency reductionAbstract
As Internet of Things (IoT) devices proliferate, edge computing has become essential for reducing latency and improving data privacy. This paper explores federated learning as a method to enhance the efficiency and security of edge computing systems. We implement and evaluate federated models in various IoT environments, demonstrating how federated learning can reduce data transfer and computation load while maintaining accuracy in data analysis.
References
Bonawitz, K., Eichner, H., Hard, A., et al. (2017). "Towards Federated Learning at Scale: System Design." Proceedings of the 2nd SysML Conference.
Chen, M., Ma, Y., Li, Y., et al. (2020). "A Survey on Federated Learning: From Model to Data." IEEE Transactions on Neural Networks and Learning Systems.
Edge Computing Consortium. (2019). "Edge Computing: A New Paradigm for Data Processing."
Hard, A., Rao, K., Mathews, R., et al. (2018). "Federated Learning for Mobile Keyboard Prediction." arXiv preprint arXiv:1811.03604.
IDC. (2020). "Worldwide DataSphere Forecast, 2020–2025."
Kairouz, P., McMahan, B., et al. (2019). "Advances and Open Problems in Federated Learning." arXiv preprint arXiv:1912.04977.
Li, T., Sahu, A. K., et al. (2020). "Federated Learning: Challenges, Methods, and Future Directions." IEEE Signal Processing Magazine.
Liu, Y., Chen, Y., et al. (2021). "Hybrid Federated Learning for Edge Computing: A Survey." IEEE Internet of Things Journal.
McMahan, H. B., Moore, E., et al. (2017). "Communication-Efficient Learning of Deep Networks from Decentralized Data." Artificial Intelligence and Statistics.
Shi, W., Yang, Y., et al. (2016). "Edge Computing: A New Frontier for Computing." IEEE Internet of Things Journal.
Statista. (2021). "Number of Connected IoT Devices Worldwide from 2019 to 2030."
Wang, J., Li, Q., et al. (2020). "Dynamic Federated Learning for Resource-Constrained IoT Devices." IEEE Transactions on Mobile Computing.
Yang, Q., Liu, Y., et al. (2019). "Federated Machine Learning: Concept and Applications." ACM Transactions on Intelligent Systems and Technology.
Zhang, Y., Wang, L., et al. (2019). "Edge Computing for Smart Cities: A Survey." IEEE Internet of Things Journal.
Zhang, Y., Wang, L., et al. (2020). "Decentralized Federated Learning: A Survey." IEEE Transactions on Neural Networks and Learning Systems.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computer Technology and Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
