Federated Learning for Vehicle Trajectory Prediction: Methodology and Benchmark Study-Reference-Cited by-同舟云学术

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Federated Learning for Vehicle Trajectory Prediction: Methodology and Benchmark Study

Published:2024-06-30 Issue: Volume:54 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Wang Hongye¹,Li Ruonan¹,Xu Zenglin¹,Li JinLong²,King Irwin³,Liu Jie¹

Affiliation:

1. Harbin Institute of Technology(Shenzhen),College of Computer Science and Technology,Shenzhen,China

2. South China University of Technology,College of Civil Engineering and Transportation,Guangzhou,China

3. The Chinese University of Hong Kong,Department of Computer Science and Engineering,Hong Kong SAR,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650788.pdf?arnumber=10650788

Reference34 articles.

1. Generalizable intention prediction of human drivers at intersections

2. AMGB: Trajectory prediction using attention-based mechanism GCN-BiLSTM in IOV

3. ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework

4. Communication-efficient learning of deep networks from decentralized data;McMahan

5. FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models

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