The prominent feature of autonomous vehicles is collecting real-time
data in the form of road images, video through on-board sensors and
cameras. Such data is then deployed to optimize the vehicular traffic
network. This paper proposes a novel framework for traffic data
optimization and network slicing in 6G. The main idea is automatically
get the training sample from the global model. Higher sample learning
accuracy is improved by deploying knowledge distillation-based training
mechanism. The traffic visual data privacy is preserved using adaptive
differential method. Experimentations are performed using vehicle and
other datasets. Simulations results show that the proposed method has
superior performance as compared with existing methods.