Flad: Federated Learning Framework Enables Autonomous Driving with Cloud-Edge-Vehicle Collaboration
Large Language Models demonstrate considerable promise for autonomous driving, but training these complex systems presents challenges related to data transmission costs and data privacy. To address these issues, Tianao Xiang, Mingjian Zhi, and colleagues from Northeastern University, alongside Lin Cai and Yuhao Chen from the University of Victoria, present a new Federated Learning framework called FLAD. This innovative system enables autonomous vehicles to collaboratively train models without directly sharing sensitive driving data, utilising a cloud-edge-vehicle architecture to minimise delays and preserve privacy. By intelligently parallelising training and employing a knowledge distillation method, FLAD optimises efficiency and personalises models according to diverse data sources, representing a significant step towards future collaborative autonomous driving and knowledge sharing.