A new approach to joint resource management in MEC-IoT based federated meta-learning

MEC and IoT are rapidly expanding technologies that offer numerous oppor tunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and la tency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading com putations task from IoT devices to MEC servers and efficiently allocating com puting resources is a complex problem. IoT tasks may have specific require ments in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our ap proach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems.