Sheng Liu

Orcid: 0000-0002-9883-5289

Affiliations:
  • KTH Royal Institute of Technology, Sweden
  • Sun Yat-sen University, China (former)


According to our database1, Sheng Liu authored at least 12 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2025
Safeguarding Federated Learning-based Road Condition Classification.
CoRR, July, 2025

2024
AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining.
IEEE Trans. Knowl. Data Eng., September, 2024

AFM3D: An Asynchronous Federated Meta-Learning Framework for Driver Distraction Detection.
IEEE Trans. Intell. Transp. Syst., August, 2024

Federated and Asynchronized Learning for Autonomous and Intelligent Things.
IEEE Netw., March, 2024

A Hybrid Control Model for Platoons at Mixed-Traffic Freeways Based on Deep Reinforcement Learning.
Proceedings of the Intelligent Robotics and Applications - 17th International Conference, 2024

2023
FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning.
Proceedings of the 22nd IEEE International Conference on Trust, 2023

Efficiency-Improved Federated Learning Approaches for Time of Arrival Estimation.
Proceedings of the 8th International Conference on Models and Technologies for Intelligent Transportation Systems, 2023

FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning.
Proceedings of the Mobile and Ubiquitous Systems: Computing, Networking and Services, 2023

2022
A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement.
IEEE Internet Things J., 2022

AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation.
Proceedings of the IEEE Smartworld, 2022

TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction.
Proceedings of the IEEE Smartworld, 2022

Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn.
Proceedings of the Knowledge Science, Engineering and Management, 2022


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