Asynchronous Federated Learning-Based Energy Scheduling for Microgrid-Enabled MEC Network

Mobile Edge Computing (MEC) is well known for its critical role in controlling the growth of Internet of Things (IoT) applications, even though energy demand forecasting is inherently challenging because of multiple task-specific factors. Including a microgrid as a backup energy source offers a chance to improve the effectiveness of energy distribution, but it also raises concerns about using non-renewable energy sources. This study explores the complex task of managing energy scheduling in a microgrid-enabled MEC network. It does this by minimizing residual planned energy using the conditional value at risk (CVaR) measure and illuminate the complexity of this NP-hard problem. To address this complex problem, a multi-agent stochastic game framework is implemented to achieve a Nash equilibrium for collective policies and prove model convergence, revealing the novel Asynchronous Federated Learning framework for Microgrid-Enabled MEC Network (AFFIN). A device selection strategy and a multi-agent Asynchronous Advantage Actor-Critic (A3C) algorithm with shared neural networks are employed to enhance learning efficiency. The proposed scheme enables localized model training and parameter sharing with the MEC server. The results demonstrated the exceptional effectiveness of incorporating CVaR into energy scheduling, surpassing the performance of random agent and single-agent models in terms of optimizing energy distribution in the MEC network.