Journal article  Open Access

Federated feature selection for cyber-physical systems of systems

Cassarà P., Gotta A., Valerio L.

Computer Science - Machine Learning  Feature selection  Human State Monitoring  Artificial Intelligence  Computer Science - Networking and Internet Architecture  Computer Networks and Communications  Internet of Things  Machine Learning  Feature Selection  Electrical and Electronic Engineering  Federated learning  Distributed learning  FOS: Computer and information sciences  Aerospace Engineering  Automotive Engineering  Federated Learning  Networking and Internet Architecture (cs.NI)  Autonomous System  Machine Learning (cs.LG)  Mutual information 

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.

Source: IEEE transactions on vehicular technology 71 (2022): 9937–9950. doi:10.1109/TVT.2022.3178612

Publisher: Institute of Electrical and Electronics Engineers,, New York , Stati Uniti d'America

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BibTeX entry
	title = {Federated feature selection for cyber-physical systems of systems},
	author = {Cassarà P. and Gotta A. and Valerio L.},
	publisher = {Institute of Electrical and Electronics Engineers,, New York , Stati Uniti d'America},
	doi = {10.1109/tvt.2022.3178612 and 10.5281/zenodo.6901226 and 10.48550/arxiv.2109.11323 and 10.5281/zenodo.6901227},
	journal = {IEEE transactions on vehicular technology},
	volume = {71},
	pages = {9937–9950},
	year = {2022}

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