Multi-Layer Distributed Learning for Intelligent Transportation Systems in 6G Aerial-Ground Integrated Networks

Federated Learning (FL) is a widely used distributed learning (DL) method for intelligent transportation systems (ITS) in the upcoming era of 6G-enabled ITS. In this work, we present the concept of Generalized Federated Split Transfer Learning (GFSTL) as a highly efficient and secure distributed learning framework for resource-limited ITS applications. The proposed GFSTL solution performs better in terms of overall training latency and accuracy and is useful for enabling ITS services in Aerial-Ground Integrated Networks (AGIN). Through comprehensive simulations carried out in vehicular scenarios, our results validate the efficacy of GFSTL on multilayered DL using Road-Side Units (RSUs) and High-Altitude Platforms (HAPs) in AGIN, demonstrating significant improvements in addressing the demands of intelligent vehicular networks. Through the integration of advanced DL techniques and the use of HAPs, our proposed framework holds promise for paving the way for an intelligent and connected vehicular network in the future.