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. 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.

2. 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.

3. 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)

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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

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

3. 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.

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

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

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

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

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

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

10. 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.