For a complete list of publications, please see Google Scholar.
We open source our code on GitHub.
Preprints
SAFARI: Sparsity-Enabled Federated Learning with Limited and Unreliable Communications
Y. Mao, Z. Zhao, M. Yang, L. Liang, Y. Liu, W. Ding, T. Lan, and X.-P. Zhang, “SAFARI: Sparsity-Enabled Federated Learning with Limited and Unreliable Communications,” IEEE Transactions on Mobile Computing, July 2023.
Age of information, latency, and reliability in intelligent vehicular networks
C. Guo, X. Wang, L. Liang, and G. Y. Li, “Age of information, latency, and reliability in intelligent vehicular networks,” IEEE Network, 2022.
Journal papers
2023
AoI-driven power allocation and batch sampling control for V2V status update communications
C. Guo, S. Liu, B. Liao, Z. Wang, and L. Liang, “AoI-driven power allocation and batch sampling control for V2V status update communications,” IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 291-302, Jan. 2024.
Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks
H. Zhang, C. Lu, H. Tang, X. Wei, L. Liang, L. Cheng, W. Ding, and Z. Han, “Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks,” IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2667-2679, Feb. 2023.
2022
2021
2020
2019
2018
Machine learning for vehicular networks: Recent advances and application examples
H. Ye, L. Liang, G. Y. Li, J. Kim, L. Lu, and M. Wu, IEEE Vehicular Technology Magazine, vol. 13, no. 2, pp. 94–101, Jun. 2018.
2017
2014 and earlier
Conference papers
Theses
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