  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, not all these data contain valuable,
and informative content but only a subset of the relative attributes should be
exploited at the Edge. Therefore, enabling AVs to locally extract such a subset
is of utmost importance to limit computation and communication workloads.
Achieving a consistent subset of data in a distributed manner imposes the AVs
to cooperate in finding an agreement on what attributes should be sent to the
Edge. In this work, we address such a problem by proposing a federated feature
selection algorithm where all the AVs collaborate to filter out, iteratively,
the redundant or irrelevant attributes in a distributed manner, without any
exchange of raw data. This solution builds on two 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. Our
federated feature selection algorithm provably converges to a solution in a
finite number of steps. Such an 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 fleet finds a consensus
with both the datasets on the minimum achievable subset of features, i.e., 24
out of 2166 (99\%) in MAV and 4 out of 8 (50\%) in WESAD, preserving the
informative content of data.
