Summary of Fedpaw: Federated Learning with Personalized Aggregation Weights For Urban Vehicle Speed Prediction, by Yuepeng He et al.
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
by Yuepeng He, Pengzhan Zhou, Yijun Zhai, Fang Qu, Zhida Qin, Mingyan Li, Songtao Guo
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A Federated learning framework with Personalized Aggregation Weights (FedPAW) is proposed to overcome the challenges of personalized vehicle speed prediction while protecting drivers’ data privacy. The method captures client-specific information by measuring the weighted mean squared error between local models and global models, allowing for tailored aggregated models to be sent to clients without additional computational or communication overhead. An LSTM-based Seq2Seq model with a multi-head attention mechanism is employed to predict future vehicle speeds using driving data collected in urban scenarios through the autonomous driving simulator CARLA. The results demonstrate that FedPAW ranks lowest in prediction error within a 10-second time horizon, achieving a 0.8% reduction in test MAE compared to eleven representative benchmark baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict car speeds is developed using artificial intelligence. This helps make self-driving cars safer by knowing what the other cars might do. The problem is that each person drives differently and has different types of cars, so we need a personalized prediction method that keeps people’s personal data private. A new framework called FedPAW does this by letting each car have its own model and sending a special version to each car without needing extra computing power or communication. This was tested using a simulator for self-driving cars and showed better results than other methods. |
Keywords
» Artificial intelligence » Federated learning » Lstm » Mae » Multi head attention » Seq2seq
