Nonlinear Decentralized Federated Localization Framework for Dynamic Vehicular Networks

Abstract

This study introduces a Nonlinear Decentralized Federated Localization Framework (DFLF) designed for precise real-time vehicle positioning in dynamic vehicular networks. The framework addresses GNSS inaccuracies, high communication costs, and privacy challenges by integrating nonlinear machine learning techniques, including Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCNs), and transformer-based attention mechanisms. These models effectively capture complex temporal and spatial dependencies from GNSS, sensor, and vehicle-to-vehicle (V2V) communication data. Federated learning ensures secure, decentralized training by exchanging encrypted model updates rather than raw data. Experimental results using synthetic GNSS data and V2V interactions demonstrate significant accuracy improvements, with root mean square errors (RMSE) of 2.3 meters in urban scenarios and 1.2 meters on highways. Scalability tests with networks of up to 500 vehicles confirm the model's robustness in dense traffic environments. Privacy-preserving measures such as differential privacy and homomorphic encryption ensure secure collaboration without notable performance degradation. The framework's nonlinear modeling capability enhances localization reliability under urban canyon conditions and intermittent connectivity, making it a viable solution for decentralized vehicle positioning in real-world scenarios.