Articolo in rivista, 2022, ENG, 10.1109/TVT.2022.3178612
Cassarà P.; Gotta A.; Valerio L.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-IIT, Pisa, Italy
Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present infor- mative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and com- munication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant at- tributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data.
IEEE transactions on vehicular technology 71 (9), pp. 9937–9950
Federated learning, Feature selection, Distributed learning, Mutual information
Gotta Alberto, Valerio Lorenzo, Cassara Pietro
IIT – Istituto di informatica e telematica, ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 471809
Year: 2022
Type: Articolo in rivista
Creation: 2022-10-07 16:33:07.000
Last update: 2022-12-14 11:52:30.000
CNR authors
EU Projects
A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence [H2020]
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics [H2020]
HumanE AI Network [H2020]
Multimodal Extreme Scale Data Analytics for Smart Cities Environments [H2020]
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:471809
DOI: 10.1109/TVT.2022.3178612
ISI Web of Science (WOS): 000854658600065