联邦学习框架提升城市车辆速度预测精度，兼顾个性化与隐私保护
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
- 通过个性化聚合权重捕捉不同车辆的驾驶特征
- 10秒内预测误差最低，测试MAE降低0.8%
- 适合智能交通、自动驾驶领域研究者参考
车辆速度预测对智能交通系统至关重要，可提升自动驾驶可靠性。由于驾驶员风格和车辆类型差异，不同目标车辆的速度预测差异显著。现有方法难以在保护数据隐私的同时实现个性化预测。本文提出联邦学习框架FedPAW，通过测量本地模型与全局模型参数间的加权均方误差，捕捉客户端特异性信息。服务器向各客户端发送定制化聚合模型，无需增加客户端计算与通信开销。在CARLA自动驾驶仿真器中采集城市场景驾驶数据，采用基于LSTM的Seq2Seq模型结合多头注意力机制预测目标车辆未来速度。实验结果表明，FedPAW在10秒时间窗口内预测误差最低，测试MAE相较11个基准方法降低0.8%。代码与数据集CarlaVSP已开源。
原文摘要 · Abstract (English)
Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing methods may not realize personalized vehicle speed prediction while protecting drivers' data privacy. We propose a Federated learning framework with Personalized Aggregation Weights (FedPAW) to overcome these challenges. This method captures client-specific information by measuring the weighted mean squared error between the parameters of local models and global models. The server sends tailored aggregated models to clients instead of a single global model, without incurring additional computational and communication overhead for clients. To evaluate the effectiveness of FedPAW, we collected driving data in urban scenarios using the autonomous driving simulator CARLA, employing an LSTM-based Seq2Seq model with a multi-head attention mechanism to predict the future speed of target vehicles. The results demonstrate that our proposed FedPAW ranks lowest in prediction error within the time horizon of 10 seconds, with a 0.8% reduction in test MAE, compared to eleven representative benchmark baselines. The source code of FedPAW and dataset CarlaVSP are open-accessed at: https://github.com/heyuepeng/PFLlibVSP and https://pan.baidu.com/s/1qs8fxUvSPERV3C9i6pfUIw?pwd=tl3e.
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