OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Federated learning‐based trajectory prediction model with privacy preserving for intelligent vehicle
Mu Han, Kai Xu, Shidian Ma, et al.
International Journal of Intelligent Systems (2022) Vol. 37, Iss. 12, pp. 10861-10879
Closed Access | Times Cited: 18
Mu Han, Kai Xu, Shidian Ma, et al.
International Journal of Intelligent Systems (2022) Vol. 37, Iss. 12, pp. 10861-10879
Closed Access | Times Cited: 18
Showing 18 citing articles:
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton, et al.
IEEE Transactions on Intelligent Vehicles (2023) Vol. 9, Iss. 1, pp. 119-137
Open Access | Times Cited: 42
Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton, et al.
IEEE Transactions on Intelligent Vehicles (2023) Vol. 9, Iss. 1, pp. 119-137
Open Access | Times Cited: 42
A Survey of Federated Learning for Connected and Automated Vehicles
Vishnu Pandi Chellapandi, Liangqi Yuan, Stanisław H. Żak, et al.
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (2023), pp. 2485-2492
Open Access | Times Cited: 26
Vishnu Pandi Chellapandi, Liangqi Yuan, Stanisław H. Żak, et al.
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (2023), pp. 2485-2492
Open Access | Times Cited: 26
A Many-objective Joint Device Selection and Aggregation Scheme for Federated Learning in IoV
Jie Wen, Zhihua Cui, Huihua Zhang, et al.
ACM Transactions on Sensor Networks (2024)
Open Access | Times Cited: 8
Jie Wen, Zhihua Cui, Huihua Zhang, et al.
ACM Transactions on Sensor Networks (2024)
Open Access | Times Cited: 8
A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method
Hongzhi Pan
Engineering Reports (2025) Vol. 7, Iss. 5
Open Access
Hongzhi Pan
Engineering Reports (2025) Vol. 7, Iss. 5
Open Access
Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles
Jie Wen, Jingbo Zhang, Zhixia Zhang, et al.
Information Sciences (2024) Vol. 664, pp. 120344-120344
Closed Access | Times Cited: 3
Jie Wen, Jingbo Zhang, Zhixia Zhang, et al.
Information Sciences (2024) Vol. 664, pp. 120344-120344
Closed Access | Times Cited: 3
QBDD: Quantum-resistant blockchain-assisted deep data deduplication protocol for vehicular crowdsensing system
Junhao Li, Qiang Nong, Ziyu Liu
Computer Networks (2024) Vol. 245, pp. 110393-110393
Closed Access | Times Cited: 3
Junhao Li, Qiang Nong, Ziyu Liu
Computer Networks (2024) Vol. 245, pp. 110393-110393
Closed Access | Times Cited: 3
A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles
Jianghui Cai, Bujia Chen, Jie Wen, et al.
Information Sciences (2024), pp. 121552-121552
Closed Access | Times Cited: 2
Jianghui Cai, Bujia Chen, Jie Wen, et al.
Information Sciences (2024), pp. 121552-121552
Closed Access | Times Cited: 2
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
Shaoheng Fang, Rui Ye, Wenhao Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 11, pp. 18321-18337
Open Access | Times Cited: 1
Shaoheng Fang, Rui Ye, Wenhao Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 11, pp. 18321-18337
Open Access | Times Cited: 1
FedVANET-TP: Federated Trajectory Prediction Model for VANETs
T. M. Sakho, Jalel Ben‐Othman
(2023) Vol. 30, pp. 1-6
Closed Access | Times Cited: 2
T. M. Sakho, Jalel Ben‐Othman
(2023) Vol. 30, pp. 1-6
Closed Access | Times Cited: 2
5G on the Roads: Latency-Optimized Federated Analytics in the Vehicular Edge
László Toka, Márk Konrad, István Pelle, et al.
IEEE Access (2023) Vol. 11, pp. 81737-81752
Open Access | Times Cited: 1
László Toka, Márk Konrad, István Pelle, et al.
IEEE Access (2023) Vol. 11, pp. 81737-81752
Open Access | Times Cited: 1
Resource-Aware Multi-Criteria Vehicle Participation for Federated Learning in Internet of Vehicles
Jie Wen, Jingbo Zhang, Zhixia Zhang, et al.
(2023)
Closed Access | Times Cited: 1
Jie Wen, Jingbo Zhang, Zhixia Zhang, et al.
(2023)
Closed Access | Times Cited: 1
Hierarchical Swarm Learning for Edge-Assisted Collaborative Vehicle Trajectory Prediction
Xuewei Hou, Lixing Chen, Junhua Tang, et al.
ICC 2022 - IEEE International Conference on Communications (2023), pp. 4144-4149
Closed Access | Times Cited: 1
Xuewei Hou, Lixing Chen, Junhua Tang, et al.
ICC 2022 - IEEE International Conference on Communications (2023), pp. 4144-4149
Closed Access | Times Cited: 1
Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies
Haoxuan Jin, Hongkuan Zhang
Applied Mathematics and Nonlinear Sciences (2024) Vol. 9, Iss. 1
Open Access
Haoxuan Jin, Hongkuan Zhang
Applied Mathematics and Nonlinear Sciences (2024) Vol. 9, Iss. 1
Open Access
On Vessel Location Forecasting and the Effect of Federated Learning
Andreas Tritsarolis, Nikos Pelekis, Konstantina Bereta, et al.
(2024), pp. 83-92
Open Access
Andreas Tritsarolis, Nikos Pelekis, Konstantina Bereta, et al.
(2024), pp. 83-92
Open Access
Federated Learning for Vehicle Trajectory Prediction: Methodology and Benchmark Study
Hongye Wang, Ruonan Li, Zenglin Xu, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2024) Vol. 54, pp. 1-8
Closed Access
Hongye Wang, Ruonan Li, Zenglin Xu, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2024) Vol. 54, pp. 1-8
Closed Access
Scenario-aware clustered federated learning for vehicle trajectory prediction with non-IID data
Liang Tao, Yangguang Cui, Xiaodong Zhang, et al.
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2024)
Closed Access
Liang Tao, Yangguang Cui, Xiaodong Zhang, et al.
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2024)
Closed Access
Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization
Wissam Kontar, Xinzhi Zhong, Soyoung Ahn
Transportation Research Record Journal of the Transportation Research Board (2024)
Open Access
Wissam Kontar, Xinzhi Zhong, Soyoung Ahn
Transportation Research Record Journal of the Transportation Research Board (2024)
Open Access
Research on the Application of Homomorphic Encryption and Federated Learning in the Internet of Vehicles Environment
Haowen Zheng
Highlights in Science Engineering and Technology (2024) Vol. 119, pp. 593-606
Open Access
Haowen Zheng
Highlights in Science Engineering and Technology (2024) Vol. 119, pp. 593-606
Open Access
Poster: Towards Realistic Federated Learning Evaluations for Connected and Automated Vehicles
Yongkang Liu, Chianing Wang, Kentaro Oguchi
(2023), pp. 244-246
Closed Access
Yongkang Liu, Chianing Wang, Kentaro Oguchi
(2023), pp. 244-246
Closed Access
