# Federated Feature Selection for Cyber-Physical Systems of Systems

arXiv: https://arxiv.org/abs/2109.11323 · HTML: https://mlsanity.com/paper/2109.11323/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Survey on Feature Selection](https://mlsanity.com/paper/1510.02892/) (2015)
- [TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence](https://mlsanity.com/paper/2107.06543/) (2021)

## Built on by (most cited first, 11 of 11)

- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Towards Interpretable Federated Learning](https://mlsanity.com/paper/2302.13473/) (2023)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks](https://mlsanity.com/paper/2205.00550/) (2022)
- [Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs](https://mlsanity.com/paper/2504.05245/) (2025)
- [Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids](https://mlsanity.com/paper/2303.08300/) (2023)
- [A Federated Channel Modeling System using Generative Neural Networks](https://mlsanity.com/paper/2305.18856/) (2023)
- [Federated Battery Diagnosis and Prognosis](https://mlsanity.com/paper/2310.09628/) (2023)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)

## Abstract (arXiv)

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.