Federated Learning-Based Secure Communication and Traffic Forecasting Framework for Distributed Renewable Microgrids

The integration of electric vehicle (EV) charging stations into renewable-rich microgrids introduces complexities in load management due to the influence of urban traffic flows on charging demand and grid stability. Traditional centralized data collection methods for forecasting traffic and energy loads raise significant privacy concerns and incur substantial communication overhead. To address these challenges, this paper presents a novel Federated Learning (FL) architecture that enables microgrid nodes—including solar inverters, battery controllers, and smart meters—and EV charging stations to collaboratively predict vehicular flow and arrival rates while preserving data privacy. The proposed framework incorporates a lightweight secure aggregation protocol over 5G/LPWAN networks to minimize communication overhead and enhance data security. Performance evaluations demonstrate that the FL-based approach achieves improved forecasting accuracy for both energy load and traffic flow, reduces communication latency and payload per FL round, and effectively preserves privacy, thereby supporting the development of resilient and sustainable energy systems.