A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms

2025-06-02

AI Summary

To address unreliable data provisioning and the privacy-utility trade-off in vertical federated learning (VFL) for traffic state estimation (TSE), this paper proposes the first reliable VFL framework tailored for TSE. We introduce a novel three-stage data provider selection mechanism and a penalty-based incentive scheme supervised by mutual information–guided game theory, enabling dynamic data quality assessment and sustained participation while preserving multi-party data privacy. Integrating road-segment sampling, mutual information modeling, and adaptive incentive design, our framework significantly enhances model robustness. Evaluated on real-world traffic datasets, it improves traffic flow and density prediction accuracy by 11.23% and 23.15%, respectively, and increases municipal departments’ per-collaboration utility by $130–$400. These results demonstrate the framework’s comprehensive advantages in reliability, fairness, and practical applicability.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework comprises two components: data provider selection and incentive mechanism design. Data provider selection is conducted in three stages to identify the most qualified MPs for VFL model training with the MA. First, the MA partitions the transportation network into road segments. Then, a mutual information (MI) model is trained for each segment to capture the relationship between data and labels. Finally, using a sampling strategy and the MI model, the MA assesses each MP's competence in data provision and selects the most qualified MP for each segment. For the incentive mechanism design, given the MA can leverage the MI mode to inspect the data quality of MP, we formulate the interaction between MA and MP as a supervision game model. Upon this, we devise a penalty-based incentive mechanism to inhibit the lazy probability of MP, thereby guaranteeing the utility of MA. Numerical simulation on real-world datasets showcased that our proposed framework augments the traffic flow and density prediction accuracy by 11.23% and 23.15% and elevates the utility of MA by 130$sim$400$ compared to the benchmark.

Problem

• Ensuring reliable VFL-based traffic estimation with data selection
• Designing incentive mechanisms to prevent MP underperformance
• Improving traffic prediction accuracy and MA utility via VFL

Innovation

• Vertical Federated Learning for traffic data privacy
• Data provider selection via mutual information model
• Penalty-based incentive mechanism for data quality