Publications
1.
Song, R.; Lyu, L.; Jiang, W.; Festag, A.; Knoll, A.
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection Journal Article
In: arXiv, 2023, (arXiv preprint arXiv:2305.11654).
Abstract | Links | BibTeX | Tags: client selection, federated learning, Intelligent Transport Systems
@article{Song:ARXIV:2023_V2XBoosted,
title = {V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection},
author = {R. Song and L. Lyu and W. Jiang and A. Festag and A. Knoll},
url = {https://arxiv.org/pdf/2305.11654},
year = {2023},
date = {2023-06-21},
urldate = {2023-06-21},
journal = {arXiv},
abstract = {Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipelineoutperforms baselines on various datasets, particularly in non-iid settings.},
note = {arXiv preprint arXiv:2305.11654},
keywords = {client selection, federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipelineoutperforms baselines on various datasets, particularly in non-iid settings.