Dongzhu Liu
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Publications

Book Chapter

  1. D. Liu, A. Sonee, O. Simeone, and S. Rini, “Differentially Private Wireless Federated Learning,” submitted, Book Chapter in Machine Learning and Wireless Communications, (editors: A. Goldsmith, D. Gunduz, H. V. Poor, and Y. Eldar), Cambridge University Press.

Journal Article

  1. M. Guo, D. Liu, O. Simeone, and D. Wen, “Low-Rank Gradient Compression with Error Feedback for MIMO Wireless Federated Learning,” submitted to IEEE Wireless Communications Letters.

  2. M. Zhang, Y. Li, D. Liu, R. Jin, G. Zhu, and C. Zhong, “Joint Compression and Deadline Optimization for Wireless Federated Learning,” to appear in IEEE Trans. Mob. Comput. [Paper]

  3. H. Xing, G. Zhu, D. Liu, H. Wen, K. Huang, and K. Wu, “Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI, ” IEEE Network, vol. 37, no. 4, pp. 135-144, Oct. 2023. [Paper]

  4. D. Liu and O. Simeone, “Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy,” IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 2946-2961, May 2023. [Paper]

  5. D. Liu and O. Simeone, “Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers,” IEEE Journal on Sel. Area Commun., vol. 40, no. 2, pp. 562-577, Oct. 2021. [Paper]

  6. D. Liu and O. Simeone, “Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control,” IEEE Journal on Sel. Area Commun., vol. 39, no. 1, pp. 170-185, Jan. 2021. [Paper]

  7. D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 406-420, Jan. 2021. [Paper]

  8. D. Liu, G. Zhu, J. Zhang, and K. Huang, “Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning,” IEEE Trans. on Cognitive Commun. Netw., vol. 7, no. 1, pp. 265-278, Mar. 2021. [Paper]

  9. G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Toward an Intelligent Edge: Wireless Communication Meets Machine Learning,” IEEE Commun. Magazine., vol. 58, no. 1, pp. 19-25, January 2020. [Paper]

  10. D. Liu and K. Huang, “Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio,” IEEE Trans. on Wireless Commun.,vol. 17, no. 4, pp 2305-2318, 2018. [Paper]

Conference Paper

  1. B. Zhang, D. Liu, O. Simeone, and G. Zhu, “Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics,” Proc. of IEEE Globecom, Kuala Lumpur, Dec. 2023.

  2. Y. Zhang, D. Liu, and O. Simeone, “Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo,” Proc. of IEEE Intl. Workshop on Signal Process. Advances in Wireless Commun. (SPAWC), Oulu, 2022. (Invited Paper)

  3. D. Liu, G. Zhu, J. Zhang, and K. Huang, “Exploiting Diversity Via Importance-Aware User Scheduling for Fast Edge Learning,” Proc. of IEEE Intl. Conf. Commun. (ICC) Workshop, Dublin, 2020.

  4. D. Liu, G. Zhu, J. Zhang, and K. Huang, “Wireless Data Acquisition for Edge Learning: Importance Aware Retransmission,” Proc. of IEEE Intl. Workshop on Signal Process. Advances in Wireless Commun. (SPAWC), Cannes, 2019. (Invited Paper)

  5. K. Huang, G. Zhu, C. You, J. Zhang, Y. Du, and D. Liu, “Communication, Computing, and Learning on the Edge,” Proc. of IEEE Intl. Conf. on Commun. Systems.(ICCS), Chengdu, 2018. (Invited Paper)

  6. D. Liu and K. Huang, “Harnessing Interference in Content Delivery by Spatial Signal Alignment,” Proc. of IEEE Globecom, Singapore, 2017.