Given the plethora of sensors with which vehicles are equipped, today’s automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning mod- els, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction – a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.

Edge-assisted Federated Learning in Vehicular Networks / LA BRUNA, Giuseppe; RISMA CARLETTI, CARLOS MATEO; Rusca, Riccardo; Casetti, CLAUDIO ETTORE; Chiasserini, Carla Fabiana; Giordanino, Marina; Tola, Roberto. - ELETTRONICO. - (2022). ((Intervento presentato al convegno The 18th International Conference on Mobility, Sensing and Networking (IEEE MSN 2022) tenutosi a Guangzhou, China (Hybrid of In-person and virtual) nel December 2022.

Edge-assisted Federated Learning in Vehicular Networks

Giuseppe La Bruna;Carlos Mateo Risma Carletti;Riccardo Rusca;Claudio Casetti;Carla Fabiana Chiasserini;
2022

Abstract

Given the plethora of sensors with which vehicles are equipped, today’s automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning mod- els, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction – a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971903