mlsanityData to 7 Oct 2026

FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction

arXiv 2412.01281 ↗First posted 2 Dec 2024cs.AI cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 2 Dec 2024.

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19 of 35 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
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  7. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  10. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  11. 2019Federated Learning with Personalization Layerscited by 533
  12. 2021Towards Personalized Federated Learningcited by 462
7 more
  1. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  2. 2020Adaptive Personalized Federated Learningcited by 353
  3. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  4. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  5. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  6. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 121
  7. 2022Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarioscited by 1

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