FedITS-tool: A lockstep co-simulation framework for vehicular federated learning with per-round energy and carbon accounting

Vehicular federated learning (VFL) under realistic mobility should be evaluated not only by model accuracy, but also by the energy and carbon cost of distributed training. Mobility, intermittent connectivity, and deadline misses can cause clients to consume energy for updates that are later dropped before aggregation, producing wasted carbon emissions. This paper presents FedITS-Tool, a modular lockstep co-simulation framework that couples SUMO-based vehicle mobility, a lightweight Veins/OMNeT++ connectivity-time service, and Flower-based FL training in Docker containers. A central orchestrator synchronizes simulation time with FL rounds, maps available clients to simulated vehicles, enforces deadline-aware commit/drop semantics, and exports per-round traces of failure causes, communication delays, energy use, and carbon emissions. Experiments show that FedITS-Tool quantifies up to 40% dropped-carbon waste in coverage-stressed scenarios while preserving traceability in 500-vehicle simulations. The tool supports reproducible studies of RSU coverage planning, deadline analysis, participation dynamics, and carbon-aware VFL design.