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Selected Publications

Book Chapter

  1. M. Tang and V. W.S. Wong, "Deep Reinforcement Learning for Mobile Edge Computing Systems," Broadband Communications, Computing, and Control for Ubiquitous Intelligence, Edited by L. Cai, B.L. Mark, J. Pan, Springer International Publishing, 2022.

Journal Publications

  1. Rongguang Ye,Wei-Bin Kou, and Ming Tang*, “Exploring Performance-Fairness Trade-offs in Federated Learning,” IEEE Transactions on Networking, 2026.

  2. ShuoWang, Bing Luo, and Ming Tang*, “An Incentive Mechanism for Federated Learning with Time-Varying Client Availability,” IEEE Transactions on Mobile Computing, 2025.

  3. Yizhou Yan, Xinyu Tang, Chao Huang, and Ming Tang*, “Incentivizing Efficient Label Denoising in Federated Learning,” IEEE Internet of Things Journal, vo1. 11, no. 23, pp.38012-38022, Dec. 2024.

  4. M. Tang, F. Peng, and V. W.S. Wong, "A Blockchain-Empowered Incentive Mechanism for Cross-Silo Federated Learning," IEEE Transactions on Mobile Computing, vol. 23, no. 10, Oct. 2024.

  5. C. Huang, M. Tang, Q. Ma, J. Huang, and X. Liu, "Promoting Collaboration in Cross-Silo Federated Learning: Challenges and Opportunities" IEEE Communications Magazine, vol. 62, no. 4, pp. 9240-9253, Apr. 2024.

  6. M. Tang and Vincent W.S. Wong, "Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems," IEEE Transactions on Mobile Computing, vol.21, no. 6, Jun. 2022. (2022 Best Paper Award from IEEE TMC)

  7. M. Tang and Vincent W.S. Wong, "Online Bitrate Selection for Viewport Adaptive 360-Degree Video Streaming," IEEE Transactions on Mobile Computing, vol. 21, no. 7, pp. 2506-2517, Jul. 2022.

  8. M. Tang and J. Huang, "How to Earn Money in Live Streaming Platforms? A Study of Donation-Based Markets," IEEE/ACM Transactions on Networking, vol. 29, no. 4, pp. 1813- 1826, Aug. 2021.

  9. M. Tang, L. Gao, and J. Huang, "Communication, Computation, and Caching Resource Sharing for Internet-of-Things," IEEE Communications Magazine, vol. 58, no. 4, pp. 75-80, Apr. 2020.

  10. M. Tang, L. Gao, and J. Huang, "Enabling Edge Cooperation in Tactile Internet via 3C Resource Sharing," IEEE Journal on Selected Areas in Communications, vol. 36, no. 11, pp. 2444-2454, Nov. 2018.

  11. M. Tang, H. Pang, S. Wang, L. Gao, J. Huang, and L. Sun, "Multi-Dimensional Auction Mechanisms for Crowdsourced Mobile Video Streaming," IEEE/ACM Transactions on Networking, vol. 26, no. 5, pp. 2062-2075, Oct. 2018.

  12. M. Tang, L. Gao, H. Pang, J. Huang and L. Sun, "Optimizations and Economics of Crowdsourced Mobile Streaming," IEEE Communications Magazine, vol. 55, no. 4, pp. 21 - 27, Apr. 2017.

Conference Publications

  1. Chuiyang Meng, Ming Tang, and Vincent W.S.Wong, ”FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment,” Proc. ICLR, 2026.

  2. Rongguang Ye, Ming Tang*, and Edith C. H. Ngai, ”On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs,” Proc. ICLR, 2026.

  3. Chen Lin, Guanghao Li, Qinglun Li, YongxianWei, HongyangWei, Li Shen, Ming Tang*, Chun Yuan*, “FedProtoAlign: Federated Prototype Alignment under Identity Inconsistency for Gait Recognition,” Proc. ICASSP, 2026.

  4. Chuiyang Meng, Ming Tang, Vincent W.S.Wong, “ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation”, Proc. IEEE INFOCOM, 2026.

  5. Ming Tang, Yuxuan Zhou, Chao Huang, “Tackling Biased Evaluators in Dueling Bandits,” Proc. NeurIPS, 2025.(Spotlight)

  6. Guanghao Li,Wenhao Jiang, Mingfeng Chen, Yan Li, Hao Yu, Shuting Dong, Tao Ren, Ming Tang*, Chun Yuan*, “SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought”, Proc. NeurIPS, 2025.

  7. Shuting Dong, Mingzhi Chen, Feng Lu, Hao Yu, Guanghao Li, ZheWu, Ming Tang*, Chun Yuan*, “VPR-Cloak: A First Look at Privacy Cloak Against Visual Place Recognition,” Proc. ICCV, 2025.

  8. R. Ye and M. Tang*, "One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models," Proc. ACL Findings, 2025.

  9. R. Ye and M. Tang*, "Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning," Proc. IJCAI, 2025.

  10. W. Kou, G. Zhu, R. Ye, S. Wang, M. Tang*, Y. Wu, "Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator," Proc. ICRA, 2025.

  11. R. Ye, W. Kou, M. Tang*, "PraFFL: A Preference-Aware Scheme in Fair Federated Learning," Proc. ACM SIGKDD, 2025.

  12. P. Han, C. Huang, G. Tian, M. Tang*, X. Liu, "Convergence Analysis of Split Federated Learning on Heterogeneous Data," Proc. NeurIPS, Dec. 2024.

  13. S. Wang, B. Luo, M. Tang*, "Tackling System-Induced Bias in Federated Learning: A Pricing-based Incentive Mechanism," Proc. IEEE ICDCS, Jul. 2024.

  14. Ming Tang and V. W.S. Wong, "Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis," Proc. IEEE INFOCOM, May 2023.

  15. M. Tang and V. W.S. Wong, "An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective," Proc. IEEE INFOCOM, May 2021.

  16. M. Tang and J. Huang, "How to Earn Money in Live Streaming Platforms?—A Study of Donation-Based Markets," Proc. IEEE INFOCOM, Apr. 2019.

  17. M. Tang, S. Wang, L. Gao, J. Huang and L. Sun, "MOMD: A Multi-Object Multi-Dimensional Auction for Crowdsourced Mobile Video Streaming," Proc. IEEE INFOCOM, May 2017.