Summary of Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network, by Muhammad Yaqub et al.
Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network
by Muhammad Yaqub, Shahzad Ahmad, Malik Abdul Manan, Imran Shabir Chuhan
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN) for real-time traffic flow prediction in Intelligent Transportation Systems (ITS). FLAGCN combines principles of asynchronous graph convolutional networks with federated learning to balance precision and computational efficiency. The model uses spatial-temporal graph convolution techniques to effectively address spatio-temporal dependencies in traffic data, while a graph federated learning technique called GraphFL facilitates efficient training. Experimental results on two distinct traffic datasets demonstrate that FLAGCN optimizes both training and inference durations while maintaining high prediction accuracy, outperforming existing models with up to 6.85% reduction in RMSE and 20.45% reduction in MAPE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better predictions for traffic flow using a new kind of computer model called FLAGCN. FLAGCN helps us predict how traffic will move in real-time, which is important for keeping roads safe and efficient. The team behind FLAGCN used a special way of combining different parts of the model to make it faster and more accurate. They tested FLAGCN on two different types of traffic data and found that it worked really well, making predictions that were almost 7% better than others. |
Keywords
* Artificial intelligence * Convolutional network * Deep learning * Federated learning * Inference * Precision
