Explainable Federated Learning for Taxi Travel Time Prediction

Jelena Fiosina

2021

Abstract

Transportation data are geographically scattered across different places, detectors, companies, or organisations and cannot be easily integrated under data privacy and related regulations. The federated learning approach helps process these data in a distributed manner, considering privacy concerns. The federated learning architecture is based mainly on deep learning, which is often more accurate than other machine learning models. However, deep-learning-based models are intransparent unexplainable black-box models, which should be explained for both users and developers. Despite the fact that extensive studies have been carried out on investigation of various model explanation methods, not enough solutions for explaining federated models exist. We propose an explainable horizontal federated learning approach, which enables processing of the distributed data while adhering to their privacy, and investigate how state-of-the-art model explanation methods can explain it. We demonstrate this approach for predicting travel time on real-world floating car data from Brunswick, Germany. The proposed approach is general and can be applied for processing data in a federated manner for other prediction and classification tasks.

Paper Citation

in Harvard Style

Fiosina J. (2021). Explainable Federated Learning for Taxi Travel Time Prediction. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 670-677. DOI: 10.5220/0010485606700677

in Bibtex Style

@conference{vehits21,
author={Jelena Fiosina},
title={Explainable Federated Learning for Taxi Travel Time Prediction},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={670-677},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010485606700677},
isbn={978-989-758-513-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Explainable Federated Learning for Taxi Travel Time Prediction
SN - 978-989-758-513-5
AU - Fiosina J.
PY - 2021
SP - 670
EP - 677
DO - 10.5220/0010485606700677