Using Federated Learning to Develop Congestion Control Solutions for Intelligent Transportation Systems
DOI:
https://doi.org/10.5753/jisa.2026.7087Keywords:
Federated Learning, Congestion Control, Federated Learning Architectures, Connected VehiclesAbstract
This article presents a comprehensive analysis of the application of Federated Learning (FL) to create congestion control and management solutions in the context of Intelligent Transportation Systems and Vehicular Networks. Starting from the types of communication in vehicular networks, which can be V2V, V2I, or V2X, the article presents three architectures for implementing federated learning in the context of connected vehicles: centralized, decentralized/hierarchical (using edge servers), and peer-to-peer architectures. Considering the variables available in vehicles that can be used to create congestion control solutions and how this information can be used in machine learning models, the article discusses the advantages and disadvantages of each architecture. Furthermore, the article presents data communication (V2V, V2I, and V2X) as a key element in the coordination of machine learning models and the possibilities for sharing information/models using the communication network. Finally, the article presents a case study illustrating traffic prediction with federated learning with a decentralized architecture composed of vehicles and edge servers in the network.
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