Abstract:
With the rapid evolution of mobile communication from 1G to 6G, efficient resource management has become essential to meet increasing demands of the networks. Traditional static allocation methods struggle to handle dynamic and heterogeneous B5G networks, leading to inefficiencies in latency, bandwidth utilization, and Quality of Service (QoS). To address these challenges, we propose an Intelligent Resource Management Framework that integrates Federated Learning (FL) with Context-Aware Statistics for optimized resource allocation. Our approach enables the training of decentralized models at the network edge using the Federated Average (FedAvg) algorithm, reducing communication overhead while preserving data privacy. Each network node locally train a model on real-time context-aware data, including user mobility, traffic variations, and signal fluctuations. The local models are then aggregated to improve learning without compromising privacy. The experimental results demonstrate superior performance, achieving 99.58% precision in resource prediction, significantly outperforming traditional centralized deep learning models. Key performance matrices such as precision, recall, F1 score, and AUC confirm the model’s ability to efficiently allocate resources under varying network conditions. The framework dynamically adjusts to traffic surges, interference, and mobility changes, ensuring optimal QoS and network stability. FD averaging algorithm performs better than other techniques. So, the proposed work achieved resource management, privacy preservation, low latency, and high data rates.
