Responsible Federated Learning in Smart Transportation: Outlooks and Challenges
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909165312540672 |
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| author | Huang, Xiaowen Huang, Tao Gu, Shushi Zhao, Shuguang Zhang, Guanglin |
| author_facet | Huang, Xiaowen Huang, Tao Gu, Shushi Zhao, Shuguang Zhang, Guanglin |
| contents | Integrating artificial intelligence (AI) and federated learning (FL) in smart transportation has raised critical issues regarding their responsible use. Ensuring responsible AI is paramount for the stability and sustainability of intelligent transportation systems. Despite its importance, research on the responsible application of AI and FL in this domain remains nascent, with a paucity of in-depth investigations into their confluence. Our study analyzes the roles of FL in smart transportation, as well as the promoting effect of responsible AI on distributed smart transportation. Lastly, we discuss the challenges of developing and implementing responsible FL in smart transportation and propose potential solutions. By integrating responsible AI and federated learning, intelligent transportation systems are expected to achieve a higher degree of intelligence, personalization, safety, and transparency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06777 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Responsible Federated Learning in Smart Transportation: Outlooks and Challenges Huang, Xiaowen Huang, Tao Gu, Shushi Zhao, Shuguang Zhang, Guanglin Networking and Internet Architecture Integrating artificial intelligence (AI) and federated learning (FL) in smart transportation has raised critical issues regarding their responsible use. Ensuring responsible AI is paramount for the stability and sustainability of intelligent transportation systems. Despite its importance, research on the responsible application of AI and FL in this domain remains nascent, with a paucity of in-depth investigations into their confluence. Our study analyzes the roles of FL in smart transportation, as well as the promoting effect of responsible AI on distributed smart transportation. Lastly, we discuss the challenges of developing and implementing responsible FL in smart transportation and propose potential solutions. By integrating responsible AI and federated learning, intelligent transportation systems are expected to achieve a higher degree of intelligence, personalization, safety, and transparency. |
| title | Responsible Federated Learning in Smart Transportation: Outlooks and Challenges |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2404.06777 |