Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

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 FL, intelligent transportation systems are expected to achieve a higher degree of intelligence, personalization, safety, and transparency.

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Publication Details

Journal
IEEE Internet of Things Magazine
Published
2024-08-23
DOI
https://doi.org/10.1109/iotm.001.2300286
Citations
6
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
2.08

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article

Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

Guanglin Zhang, Shushi Gu, Xiaowen Huang, Shuguang Zhao et al.
6 citations
IEEE Internet of Things Magazine
Privacy-Preserving Technologies in Data
2.08
article

Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

Guanglin Zhang, Shushi Gu, Xiaowen Huang, Shuguang Zhao, Tao Huang
article en
6 citations

Abstract

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 FL, intelligent transportation systems are expected to achieve a higher degree of intelligence, personalization, safety, and transparency.

IEEE Internet of Things MagazineVol. 7(5)
Donghua University (CN), Harbin Institute of Technology (CN), James Cook University (AU)
National Natural Science Foundation of China, Program of Shanghai Academic Research Leader, Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Privacy-Preserving Technologies in Data
2.08
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Responsible Federated Learning in Smart Transportation: Outlooks and Challenges — Guanglin Zhang, Shushi Gu, et al. · IEEE Internet of Things Magazine (2024) | TGRS Research Map | TGRS