Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Responsible Federated Learning in Smart Transportation: Outlooks and Challenges (2404.06777v1)

Published 10 Apr 2024 in cs.NI

Abstract: Integrating 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Xiaowen Huang (12 papers)
  2. Tao Huang (203 papers)
  3. Shushi Gu (3 papers)
  4. Shuguang Zhao (2 papers)
  5. Guanglin Zhang (5 papers)
Citations (2)

Summary

We haven't generated a summary for this paper yet.