Published July 25, 2022 | Version v1
Journal article Open

Federated Feature Selection for Cyber-Physical Systems of Systems

  • 1. National Research Council, Institute of Information Science and Technologies, Pisa
  • 2. National Research Council, Institute of Information Science and Technologies, Pisa, Italy
  • 3. National Research Council, Institute of Informatics and Telematics, 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 informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication 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 attributes in a distributed manner, without any exchange of raw data, through 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.


This work is partially supported by the MIUR PON project OK-INSAID (GA #ARS01_00917) and by the H2020 projects TEACHING (GA #871385), HumanAI-Net, (GA #952026), MARVEL (GA #957337), and So-BigData++ (GA #871042).



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TEACHING – A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence 871385
European Commission
MARVEL – Multimodal Extreme Scale Data Analytics for Smart Cities Environments 957337
European Commission
SoBigData-PlusPlus – SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics 871042
European Commission
HumanE-AI-Net – HumanE AI Network 952026
European Commission