Abstract:
With the rapid expansion of intelligent devices and the growing demand for real-time data processing, massive volumes of data are now generated and stored at the network edge rather than in centralized clouds. To ensure data privacy while enabling collaborative model training, Federated Learning (FL) has emerged as a promising paradigm that allows devices to train local models using private data and share only model parameters with a central server. However, due to limited wireless bandwidth and constrained energy resources in mobile devices, it is impractical for FL to perform model updates and aggregation across all participating devices simultaneously. Furthermore, selecting appropriate devices for each training round is a critical challenge that directly impacts resource utilization, energy efficiency, and communication latency. In 5G-enabled Mobile Edge Computing (MEC) environments supporting Machine Type Communication (MTC), these challenges are further amplified by device heterogeneity and dynamic network conditions, making efficient coordination and resource management essential for maintaining performance and scalability. To address these issues, we propose an adaptive resource management framework for MEC-assisted FL that integrates intelligent device selection and dynamic resource allocation using a Double Deep Q-Network (DDQN). The device selection process is modeled as a Markov Decision Process (MDP), allowing the DDQN to make dynamic and experience-driven decisions that balance energy consumption, computational load, and bandwidth utilization under varying network and device conditions. By combining 5th generation network (5GN) capabilities with FL on the MNIST dataset, the framework achieves an intelligent, low-latency, and energy-efficient edge learning environment—paving the way for scalable, privacy-preserving, and real-time distributed Artificial Intelligence (AI) systems for MTC applications.
