EFL-ITS: An Energy-Efficient Learning Framework for Load Balancing in SDN-Enabled Intelligent Transportation Systems

In energy-constrained intelligent transportation systems (ITS), ensuring efficient, secure, and adaptive network communications is critical for real-time decision-making and low-latency services. Conventional normalized graph cut models have been used in distributed software-defined networking (SDN) environments to address challenges of resource overhead, energy consumption, routing, and data privacy. However, they often suffer from high computational cost and limited adaptability to dynamic traffic. To this end, we propose EFL-ITS, an energy-efficient learning framework for load balancing in software-defined networking enabled intelligent transportation systems. The framework collects valid traffic by first partitioning the global network into subgraphs via normalized graph cuts and then performing anomaly detection within each subgraph using graph attention networks. Then, subgraph-level traffic prediction is employed for analysis, followed by Q-learning-based agents to perform multi-metric path selection. Finally, federated learning aggregates local models to reduce communication overhead and protect privacy. Simulation results on SDN topologies show that EFL-ITS significantly improves throughput, delay, packet loss, and load balancing, while maintaining low energy consumption—validating its efficiency and scalability for ITS.