Control is a critical module of autonomous driving systems, which ensures safety and enhances the human-machine interface. Due to the diverse control demands dictated by different driving scenarios, autonomous vehicles require a data-intensive, adaptive, and intelligent controller. To speed up the control process and improve the performance in different scenarios, we introduce a novelty federated learning framework FedMG, which efficiently coordinates diverse vehicles to train a collaboratively models while preserving data privacy to tune the control process. Through detailed analysis of driving scenarios, vehicles are clustered to different groups based on driving scenarios to seek a balance between data quality and communication efficiency. It enables the consolidation of several global models, each optimized for peak performance, thereby enhancing the overall system's effectiveness. Extensive experiments with different numbers of vehicles and a variety of driving scenarios demonstrate the effectiveness of FedMG. The framework significantly reduces cumulative driving errors, achieving reductions ranging from 5.42% to 76.43%, while improving user comfort, with improvements ranging from 2.23% to 34.61% over baselines.