PSFL: Personalized Split Federated Learning Framework for Distributed Model Training in Intelligent Transportation Systems

Interest in Intelligent Transportation Systems (ITS) has increased significantly with the development of 6G. Owning an extremely high transmission speed, 6G is able to support low-latency service for edge-intelligence applications by Machine Learning(ML) techniques. However, traditional centralized learning is not suitable for this scenario due to the requirement for users to upload local data to the server, which can compromise data privacy. To overcome this challenge, Federated Learning (FL) and Split Learning (SL), as progressive distributed learning techniques, have been proposed as a solution. They enable the training of ML models while preserving data privacy. However, conventional FL has poor convergence when data heterogeneity occurs, also fails to meet personalized demands. To address these issues, We propose a novel personalized Federated Learning(pFL) framework, which trains models in SL and collaborates in FL. It offers a personalized solution for each client while retaining a global solution for newcomers. Experimental results demonstrate that our method outperforms other advanced baselines on benchmark datasets.