Zhengming Zhang

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Ph.D student in information and communication engineering with the School of Information Science and Engineering Southeast University

View the Project on GitHub jhcknzzm/zhengming.github.io

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About Me

Zhengming Zhang received the B.S. in electronic information science and technology from Nanjing Agricultural University, Nanjing, China, in 2016. Since September 2016, he is pursuing his PhD degree in information and communication engineering with the School of Information Science and Engineering, Southeast University. His current research interests include deep Learning, federated Learning, semi-supervised Learning, wireless big data, and 5G mobile networks.

Publications

  1. Poison Neural Network-Based mmWave Beam Selection and Detoxification with Machine Unlearning

    Z. Zhang, M. Tian, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Communications. paper

  2. Backdoor Federated Learning-Based mmWave Beam Selection

    Z. Zhang, R. Yang, X. Zhang, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Communications. paper

  3. A Self-Supervised Learning-Based Channel Estimation for IRS-Aided Communication Without Ground Truth

    Z. Zhang, T. Ji, H. Shi, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Wireless Communications. paper and code

  4. Neurotoxin: Durable Backdoors in Federated Learning

    Z. Zhang, A. Panda, L. Song, Y. Yang, M. Mahoney, P. Mittal, R. Kannan, J. Gonzalez, in International Conference on Machine Learning, 2022. paper and code

  5. Beyond Supervised Power Control in Massive MIMO Network: Simple Deep Neural Network Solutions

    Z. Zhang, M. Hua, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Vehicular Technology. paper

  6. Improving semi-supervised federated learning by reducing the gradient diversity of models

    Z. Zhang, Z. Yao, Y. Yang, Y. Yan, J. E. Gonzalez, and M. W. Mahoney. paper and code

  7. Double Coded Caching in Ultra Dense Networks: Caching and Multicast Scheduling via Deep Reinforcement Learning

    Z. Zhang, H. Chen, M. Hua, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Communications, vol. 68, no. 2, pp. 1071-1086, Feb. 2020, doi: 10.1109/TCOMM.2019.2955490. paper

  8. Proactive Caching for Vehicular Multi-View 3D Video Streaming via Deep Reinforcement Learning

    Z. Zhang, Y. Yang, M. Hua, C. Li, Y. Huang and L. Yang, in IEEE Transactions on Wireless Communications, vol. 18, no. 5, pp. 2693-2706, May 2019, doi: 10.1109/TWC.2019.2907077. paper

  9. Placement Delivery Array Design via Attention-Based Sequence-to-Sequence Model With Deep Neural Network

    Z. Zhang, M. Hua, C. Li, Y. Huang and L. Yang, in IEEE Wireless Communications Letters, vol. 8, no. 2, pp. 372-375, April 2019, doi: 10.1109/LWC.2018.2873334. paper

  10. On the Cover Problem for Coded Caching in Wireless Networks via Deep Neural Network

    Z. Zhang, Y. Zheng, C. Li, Y. Huang and L. Yang, 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6, doi: 10.1109/GLOBECOM38437.2019.9013459. paper

  11. Cache-Enabled Adaptive Bit Rate Streaming via Deep Self-Transfer Reinforcement Learning

    Z. Zhang, Y. Zheng, C. Li, Y. Huang and L. Yang, 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, 2018, pp. 1-6, doi: 10.1109/WCSP.2018.8555916. paper

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