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Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

Xiaowen Huang, Tao Huang, Shushi Gu, Shuguang Zhao, Guanglin Zhang

TL;DR

The paper addresses the underexplored intersection of responsible AI and Federated Learning (FL) in smart transportation. It surveys FL services and responsible AI services, proposing a combined responsible FL framework with a structured service taxonomy and governance to guide deployment. It identifies challenges—ethical data usage, bias, transparency, accountability, regulatory compliance, participation inequality, monitoring, and stakeholder education—and offers potential solutions to enable practical, trustworthy ITS deployments. The work aims to improve privacy, fairness, safety, transparency, sustainability, and stakeholder trust in intelligent transportation systems through an integrated, governance-aware approach.

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.

Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

TL;DR

The paper addresses the underexplored intersection of responsible AI and Federated Learning (FL) in smart transportation. It surveys FL services and responsible AI services, proposing a combined responsible FL framework with a structured service taxonomy and governance to guide deployment. It identifies challenges—ethical data usage, bias, transparency, accountability, regulatory compliance, participation inequality, monitoring, and stakeholder education—and offers potential solutions to enable practical, trustworthy ITS deployments. The work aims to improve privacy, fairness, safety, transparency, sustainability, and stakeholder trust in intelligent transportation systems through an integrated, governance-aware approach.

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.
Paper Structure (31 sections, 6 figures)

This paper contains 31 sections, 6 figures.

Table of Contents

  1. Introduction
  2. FL Services for Smart Transportation
  3. Privacy
  4. Personalized Service
  5. Collaborative Knowledge Sharing
  6. Real-time Decision Making
  7. Improved Model Accuracy
  8. Scalability and Resource Efficiency
  9. Responsible AI Services for Smart Transportation
  10. Privacy Protection Services
  11. Fairness and Transparency Services
  12. Interpretability and Reliability Services
  13. Safety and Synergy Services
  14. Ethical Review and Regulatory Services
  15. Responsible FL Service for Smart Transportation
  16. ...and 16 more sections

Figures (6)

  • Figure 1: The examples of responsible AI and FL in smart transportation.
  • Figure 2: Advantages of FL for smart transportation.
  • Figure 3: Advantages of responsible AI for smart transportation.
  • Figure 4: Advantages of responsible FL for smart transportation.
  • Figure 5: Journey from FL to responsible FL.
  • ...and 1 more figures