Intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. They are therefore expected not just to consume ITS services, but also to actively contribute to the implemen- tation of relevant ITS applications. With an increasing role of machine learning (ML) approaches, vehicles are called to put into use their computing capabilities and sensed data for the training of ML models. This can be enacted through distributed learning approaches, which however may lead to significant communication overhead or to learners converging to different models. In this work, we envision a new distributed learning scheme, named EAGLE, that, with the assistance of the network edge, aims at exploiting the vehicles’ data and computing capabilities, while enabling an efficient learning process. To this end, EAGLE combines the advantages of two existing schemes, namely, federated learning and gossiping learning, yielding a distributed paradigm that ensures both scalability and model consistency. Our results, obtained using two different real-world data sets, show that EAGLE can improve learning accuracy by 20%, while reducing the communication overhead by 45%.

Edge-assisted Gossiping Learning: Leveraging V2V Communications between Connected Vehicles / Di Giacomo, G.; Haerri, Jerome; Chiasserini, Carla Fabiana. - STAMPA. - (2022). ((Intervento presentato al convegno 25th IEEE Intelligent Transportation Systems Conference (ITSC 2022) tenutosi a Macau, China nel October 2022.

Edge-assisted Gossiping Learning: Leveraging V2V Communications between Connected Vehicles

G. Di Giacomo;Carla Fabiana Chiasserini
2022

Abstract

Intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. They are therefore expected not just to consume ITS services, but also to actively contribute to the implemen- tation of relevant ITS applications. With an increasing role of machine learning (ML) approaches, vehicles are called to put into use their computing capabilities and sensed data for the training of ML models. This can be enacted through distributed learning approaches, which however may lead to significant communication overhead or to learners converging to different models. In this work, we envision a new distributed learning scheme, named EAGLE, that, with the assistance of the network edge, aims at exploiting the vehicles’ data and computing capabilities, while enabling an efficient learning process. To this end, EAGLE combines the advantages of two existing schemes, namely, federated learning and gossiping learning, yielding a distributed paradigm that ensures both scalability and model consistency. Our results, obtained using two different real-world data sets, show that EAGLE can improve learning accuracy by 20%, while reducing the communication overhead by 45%.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2968099