Summary of Refol: Resource-efficient Federated Online Learning For Traffic Flow Forecasting, by Qingxiang Liu et al.
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
by Qingxiang Liu, Sheng Sun, Yuxuan Liang, Xiaolong Xu, Min Liu, Muhammad Bilal, Yuwei Wang, Xujing Li, Yu Zheng
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Resource-Efficient Federated Online Learning (REFOL) method for traffic flow forecasting (TFF) aims to overcome limitations of existing federated learning approaches. By detecting concept drift during model training, online learning can better handle distribution shifts between historical and future data. REFOL guarantees prediction performance while reducing communication-lightweight and computation-efficiently. The method incorporates a client participation mechanism to detect concept drift, an adaptive online optimization strategy to avoid meaningless updates, and a graph convolution-based model aggregation mechanism to assess participant contribution without extra resource consumption. Experimental results on real-world datasets demonstrate the superiority of REFOL in terms of prediction improvement and resource economization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning methods for traffic flow forecasting (TFF) are designed to avoid sharing sensitive data. However, existing approaches may not work well when the pattern of traffic changes. The proposed REFOL method solves this problem by detecting changes during training and updating models accordingly. It also makes sure that computers don’t waste time and resources on unnecessary updates. The method uses a combination of techniques to achieve better results while using fewer resources. |
Keywords
» Artificial intelligence » Federated learning » Online learning » Optimization
