NEO: Federated Learning with Dynamic Meta-Learning for Vehicular Networks

• Carnot Braun UNICAMP http://orcid.org/0009-0006-3691-7584
• Rafael O. Jarczewski UNICAMP https://orcid.org/0000-0002-2062-8976
• Joahannes B. D. da Costa UNIFESP https://orcid.org/0000-0001-9973-2479
• Allan M. de Souza UNICAMP https://orcid.org/0000-0002-5518-8392

DOI: https://doi.org/10.5753/courb.2025.9588

Abstract

Vehicular Ad Hoc Networks (VANETs) enable direct communication between vehicles and infrastructure, contributing to traffic safety and efficiency. However, challenges such as high mobility and computational constraints hinder the application of Machine Learning (ML) techniques in this context. Federated Learning (FL) allows for the distributed training of models while preserving data privacy, but it often requires a large number of communication rounds to achieve good convergence. In this regard, this work proposes an adaptive Federated Meta-Learning approach based on Graph Convolutional Networks (GCNs) for predicting speed trends in VANETs. The proposed solution dynamically adjusts the intensity of Meta-Learning, optimizing training efficiency and reducing computational overhead. Experimental results indicate that the approach reduces prediction error by 5% and decreases training time by 8% when compared to other state-of-the-art methods, making it a viable alternative for resource-constrained VANET applications.

Keywords: Federated Learning for Urban Computing, Smart Cities, Internet of Things, Meta-Learning, Urban Planning Using Big Data, Intelligent Transportation Systems

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