# A V2X-based Privacy Preserving Federated Measuring and Learning System

arXiv: https://arxiv.org/abs/2401.13848 · HTML: https://mlsanity.com/paper/2401.13848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [Efficient Decentralized Deep Learning by Dynamic Model Averaging](https://mlsanity.com/paper/1807.03210/) (2018)
- [Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS](https://mlsanity.com/paper/2204.00215/) (2022)
- [A Survey of Security and Privacy Issues in V2X Communication Systems](https://mlsanity.com/paper/2208.14674/) (2022)
- [Benefits of V2V Communication for Autonomous and Connected Vehicles](https://mlsanity.com/paper/1803.02900/) (2018)

## Abstract (arXiv)

Future autonomous vehicles (AVs) will use a variety of sensors that generate a vast amount of data. Naturally, this data not only serves self-driving algorithms; but can also assist other vehicles or the infrastructure in real-time decision-making. Consequently, vehicles shall exchange their measurement data over Vehicle-to-Everything (V2X) technologies. Moreover, predicting the state of the road network might be beneficial too. With such a prediction, we might mitigate road congestion, balance parking lot usage, or optimize the traffic flow. That would decrease transportation costs as well as reduce its environmental impact. In this paper, we propose a federated measurement and learning system that provides real-time data to fellow vehicles over Vehicle-to-Vehicle (V2V) communication while also operating a federated learning (FL) scheme over the Vehicle-to-Network (V2N) link to create a predictive model of the transportation network. As we are yet to have real-world AV data, we model it with a non-IID (independent and identically distributed) dataset to evaluate the capabilities of the proposed system in terms of performance and privacy. Results indicate that the proposed FL scheme improves learning performance and prevents eavesdropping at the aggregator server side.