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Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective

arXiv 2503.11963 ↗First posted 15 Mar 2025cs.LG cs.CRcited by 0

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Abstract (arXiv)

Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to data-scarce cities without traffic data exchange, existing approaches in Federated Traffic Knowledge Transfer (FTT) still face several critical challenges such as potential privacy leakage, cross-city data distribution discrepancies, and low data quality, hindering their practical application in real-world scenarios. To this end, we present FedTT, a novel privacy-aware and efficient federated learning framework for cross-city traffic knowledge transfer. Specifically, our proposed framework includes three key innovations: (i) a traffic view imputation method for missing traffic data completion to enhance data quality, (ii) a traffic domain adapter for uniform traffic data transformation to address data distribution discrepancies, and (iii) a traffic secret aggregation protocol for secure traffic data aggregation to safeguard data privacy. Extensive experiments on 4 real-world datasets demonstrate that the proposed FedTT framework outperforms the 14 state-of-the-art baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 67 references that are in the map. First posted on arXiv 15 Mar 2025.

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References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  5. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  6. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  7. 2022Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfercited by 43
  8. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  9. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
  10. 2021Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputationcited by 35
  11. 2023Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bankcited by 22
  12. 2024FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detectioncited by 8
5 more
  1. 2018Reuse and Adaptation for Entity Resolution through Transfer Learningcited by 7
  2. 2024Towards Eliminating Hard Label Constraints in Gradient Inversion Attackscited by 4
  3. 2022Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biasescited by 3
  4. 2024Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attackscited by 2
  5. 2023Efficient and Private Federated Trajectory Matchingcited by 1

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