Improved Knowledge Mapping in Heterogeneous Network Using Enhanced Federated Learning

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Yelithoti Sravana Kumar, Tapaswini Samant, Swati Swayamsiddha

10.14445/23488549/IJECE-V12I5P117

How to Cite?

Yelithoti Sravana Kumar, Tapaswini Samant, Swati Swayamsiddha, "Improved Knowledge Mapping in Heterogeneous Network Using Enhanced Federated Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 5, pp. 200-209, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I5P117

Abstract:

Edge networks consist of devices that analyze different data types and models. A knowledge map can be developed in device scheduling using homogeneous or heterogeneous models. In this study, a Federated learning algorithm is used to minimize communication overhead by distributing device information in batches, ensuring that all devices have an equal opportunity. The federated learning method is utilized in this study to allocate spectrum between primary and secondary users by categorizing them using standard parameters such as bandwidth utilization, energy, and a novel metric known as signal-to-noise ratio. Here, the spectrum is allocated using the deep learning technique. However, in other algorithms, global loss minimization was not considered for the model analysis. Fortunately, in this study, model analysis was carried out using deep learning architectures such as Convolution Neural Networks for feature extraction, pooling layers for downsampling, and accessing the performance using evaluation metrics. The findings indicated that knowledge mapping could also be improved by improving the model.

Keywords:

Heterogeneous network, 5G, Deep learning, Knowledge mapping, Cognitive radio network, Spectrum allocation, Federated Learning.

References:

[1] Luis A. Fletscher et al., “Energy-Aware Resource Management in Heterogeneous Cellular Networks with Hybrid Energy Sources,” IEEE Transactions on Network and Service Management, vol. 16, no. 1, pp. 279-293, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xiaoge Huang et al., “Dynamic Femtocell gNB On/Off Strategies and Seamless Dual Connectivity in 5G Heterogeneous Cellular Networks,” IEEE Access, vol. 6, pp. 21359-21368, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Nan Zhao et al., “Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Networks,” 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hideo Kobayashi et al., “Towards Sustainable Heterogeneous Wireless Networks: A Decision Strategy for AP Selection with Dynamic Graphs,” Computer Networks, vol. 132, pp. 99-107, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jingjing Wang et al., “Distributed Q-Learning Aided Heterogeneous Network Association for Energy-Efficient IIoT,” IEEE Transactions on Industrial Informatics, vol. 16, no. 4, pp. 2756-2764, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Rim Haw et al., “Cache Aware User Association for Wireless Heterogeneous Networks,” IEEE Access, vol. 7, pp. 3472-3485, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yali Chen et al., “Resource Allocation for Device-to-Device Communications in Multi-Cell Multi-Band Heterogeneous Cellular Networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4760-4773, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Yaohua Sun et al., “Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3072-3108, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chaoyun Zhang, Paul Patras, and Hamed Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mingzhe Chen et al., “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039-3071, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Wei Yang Bryan Lim et al., “Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9575-9588, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Haijun Zhang et al., “Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2406-2415, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yun Li et al., “Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks,” IEEE Transactions on Mobile Computing, vol. 21, no. 6, pp. 2130-2142, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Canh T. Dinh et al., “Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation,” IEEE/ACM Transactions on Networking, vol. 29, no. 1, pp. 398-409, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chen Zhang et al., “A Survey on Federated Learning,” Knowledge-Based Systems, vol. 216, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Wei Yang Bryan Lim et al., “Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3640-3653, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Xiaokang Zhou et al., “Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5308-5317, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Rami Hamdi et al., “Federated Learning Over Energy Harvesting Wireless Networks,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 92-103, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Pushpa Singh et al., Federated Learning: Challenges, Methods, and Future Directions, Federated Learning for IoT Applications, Springer, Cham, pp. 199-214, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sepehr Ashtari et al., “Knowledge-Defined Networking: Applications, Challenges and Future Work,” Array, vol. 14, pp. 1-48, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Zhixiong Chen et al., “Knowledge-Aided Federated Learning for Energy-Limited Wireless Networks,” IEEE Transactions on Communications, vol. 71, no. 6, pp. 3368-3386, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Jinglong Shen et al., “Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, pp. 5847-5852, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Wenlong Song et al., “A Federated Learning Scheme Based on Lightweight Differential Privacy,” 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, pp. 2356-2361, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Katharine Daly et al., “Federated Learning in Practice: Reflections and Projections,” 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, Washington, DC, USA, pp. 148-156, 2024.[CrossRef] [Google Scholar] [Publisher Link]