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.
Authors
- Guanglin Zhang (ORCID: https://orcid.org/0000-0003-4095-6843)
- Shushi Gu (ORCID: https://orcid.org/0000-0002-3897-5407)
- Xiaowen Huang (ORCID: https://orcid.org/0000-0001-6323-7070)
- Shuguang Zhao (ORCID: https://orcid.org/0000-0002-6359-4091)
- Tao Huang (ORCID: https://orcid.org/0000-0002-8098-8906)
Institutions
- Donghua University (CN)
- Harbin Institute of Technology (CN)
- James Cook University (AU)
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
Funders
- 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