Federated Feature Selection for Cyber-Physical Systems of Systems

TL;DR

This paper introduces a federated feature selection algorithm for autonomous vehicles that collaboratively identifies the most relevant data attributes locally, reducing communication and computation while maintaining data informativeness.

Contribution

It proposes a novel federated feature selection method combining mutual information and Bayesian aggregation, ensuring convergence without raw data exchange.

Findings

Achieved 99% feature reduction in MAV dataset while preserving data quality.

Reduced features to 50% in WESAD dataset, maintaining informative content.

Algorithm converges in finite steps, enabling efficient distributed feature selection.

Abstract

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.