A Federated Learning Approach for Predicting Resource Allocation in Multi-Access Edge Computing (MEC) |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Authors : Ramesh Kasarla | ||
| DOI : 10.14445/22312803/IJCTT-V73I5P114 | ||
How to Cite?
Ramesh Kasarla, "A Federated Learning Approach for Predicting Resource Allocation in Multi-Access Edge Computing (MEC)," International Journal of Computer Trends and Technology, vol. 73, no. 5, pp. 101-112, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P114
Abstract
In modern networks, where stronger ultra-low latency and data throughput are needed, Multi-Access Edge Computing (MEC) becomes a necessary architecture for 5G/6G networks that support real-time applications. Nevertheless, a dynamic edge ecosystem, diverse device properties, and privacy preservation needs interfere with MEC resource management. This paper proposes a new Federated Learning (FL) framework to predict resource allocation in MEC that removes such barriers by enabling decentralized model training to be performed directly at the network edge. In contradiction to conventional centralized strategies, our approach significantly reduces communication costs by up to 90% while providing competitive performance due to the efficient use of non-IID data at edge locations. Feeding lightweight CNNs and reducing the whole energy demand is achieved by the balanced computational requirements in the design aggregation through FedOpt aggregation. Based on the results of outcome analysis on MNIST and Fashion-MNIST, we observe accelerated convergence, increased energy savings and performance scalability, where energy consumption per training round is 29% lower than in centralized systems. This approach shows impressive results in processing non-IID data due to reliable performance on different edge devices. Such discoveries show that FL has a high potential to transform MEC resource allocation and thus contribute to more adaptive, protected, and efficient edge computing architecture.
Keywords
Federated Learning, Multi-Access Edge Computing, Resource Allocation, 5G/6G Networks, Non-IID Data, Distributed Machine Learning, Energy Efficiency, Model Aggregation.
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