FLEXE: Investigating Federated Learning in Connected Autonomous Vehicle Simulations
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Due to the increased computational capacity of Connected and Autonomous Vehicles (CAVs) and worries about transferring private information, it is becoming more and more appealing to store data locally and move network computing to the edge. This trend also extends to Machine Learning (ML) where Federated learning (FL) has emerged as an attractive solution for preserving privacy. Today, to evaluate the implemented vehicular FL mechanisms for ML training, researchers often disregard the impact of CAV mobility, network topology dynamics, or communication patterns, all of which have a large impact on the final system performance. To address this, this work presents FLEXE, an Open Source extension to Veins that offers researchers a simulation environment to run FL experiments in realistic scenarios. FLEXE combines the popular Veins framework with the OpenCV library. Using the example of traffic sign recognition, we demonstrate how FLEXE can support investigations of FL techniques in a vehicular environment.
Details
Original language | English |
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Title of host publication | 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings |
Place of Publication | London, United Kingdom |
Number of pages | 5 |
ISBN (electronic) | 9781665454681 |
Publication status | Published - Sept 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85147011726 |
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Bibtex | nsm-lobato2022flexe |