A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities
- DOI
- 10.1038/s41598-024-52181-6
- Published
- 2024-01-24
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-024-52181-6,
title = {A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities},
author = {Adeel Munawar and Mongkut Piantanakulchai},
year = {2024},
journal = {Scientific Reports},
doi = {10.1038/s41598-024-52181-6},
url = {https://doi.org/10.1038/s41598-024-52181-6}
}RIS
TY - JOUR TI - A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities AU - Adeel Munawar AU - Mongkut Piantanakulchai PY - 2024 JO - Scientific Reports DO - 10.1038/s41598-024-52181-6 UR - https://doi.org/10.1038/s41598-024-52181-6 ER -
APA
Munawar, A., & Piantanakulchai, M. (2024). A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities. Scientific Reports. https://doi.org/10.1038/s41598-024-52181-6
Source records
- crossref · retrieved 2026-09-25T09:29:42.724Z