Splitfed learning for smart transportation

This chapter explores the application of splitfed learning in the realm of smart transportation, focusing on its ability to enhance the privacy and efficiency of automated transportation systems. Splitfed learning is a novel approach that allows vehicles to train on a shared model while preserving the privacy of their local data. By leveraging this technique, intelligent transportation projects can effectively utilize sensitive or private data without compromising the privacy of individual users. This chapter delves into the potential benefits and applications of splitfed learning in various aspects of smart transportation such as roadway forecasting and congestion mitigation.