Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

TL;DR

This paper explores the integration of federated learning and responsible AI in smart transportation, highlighting challenges and proposing solutions to enhance safety, transparency, and personalization in intelligent systems.

Contribution

It provides an analysis of federated learning's role in smart transportation and discusses the challenges of implementing responsible AI within this context.

Findings

Federated learning enhances personalization and safety in smart transportation.

Responsible AI promotes transparency and trust in intelligent transportation systems.

Identifies key challenges and proposes potential solutions for responsible federated learning.

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