Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation
Fermin Orozco, Pedro Porto Buarque de Gusmão, Hongkai Wen, Johan Wahlström, Man Luo·December 11, 2024
Summary
FedTPS, a federated learning framework, enhances traffic flow prediction using synthetic data augmentation. It addresses privacy and commercial sensitivity by enabling decentralized collaboration among stakeholders. FedTPS generates synthetic data to augment local datasets, improving model performance. Evaluated on a real-world ride-sharing dataset, FedTPS outperforms other FL baselines. Its main contributions include a novel traffic prediction model, GATAU, and the FedTPS framework, validated on a real-world dataset showing improved results compared to other FL frameworks.
Introduction
Background
Overview of federated learning (FL) and its applications in traffic flow prediction
Importance of privacy and commercial sensitivity in traffic data
Objective
To present FedTPS, a federated learning framework that uses synthetic data augmentation for traffic flow prediction
To demonstrate how FedTPS addresses privacy concerns and improves model performance through decentralized collaboration
Method
Data Collection
Description of the real-world ride-sharing dataset used for evaluation
Explanation of how FedTPS collects data from various stakeholders
Data Preprocessing
Techniques for preparing and cleaning the collected data
Integration of synthetic data generation into the preprocessing pipeline
FedTPS Framework
Architecture
Overview of the FedTPS architecture, including components and their functions
Explanation of how FedTPS enables decentralized collaboration among stakeholders
Synthetic Data Generation
Description of the GATAU model for generating synthetic traffic data
Process of augmenting local datasets with synthetic data
Model Training
Explanation of the federated learning process in FedTPS
How the model is trained using decentralized data
Evaluation
Baseline Comparison
Overview of other federated learning baselines used for comparison
Metrics for evaluating model performance
Results
Detailed results of FedTPS on the real-world ride-sharing dataset
Comparison of FedTPS with other FL baselines
Contributions
GATAU Model
Description of the GATAU model and its role in traffic prediction
FedTPS Framework
Explanation of how FedTPS improves upon existing federated learning approaches
Validation of FedTPS on a real-world dataset
Conclusion
Summary of FedTPS's achievements
Future Work
Potential improvements and extensions of the FedTPS framework
Areas for further research in federated learning for traffic flow prediction
Basic info
papers
distributed, parallel, and cluster computing
machine learning
artificial intelligence
Advanced features
Insights
How does FedTPS address privacy and commercial sensitivity in traffic prediction?
What is FedTPS and how does it enhance traffic flow prediction?
How does FedTPS outperform other federated learning baselines in traffic prediction tasks?
What are the main contributions of FedTPS, and how are they validated?