| Dynamic compression ratio selection for edge inference systems with hard deadlines X Huang, S Zhou IEEE Internet of Things Journal 7 (9), 8800-8810, 2020 | 26 | 2020 |
| Edge learning with timeliness constraints: Challenges and solutions Y Sun, W Shi, X Huang, S Zhou, Z Niu IEEE communications magazine 58 (12), 27-33, 2020 | 19 | 2020 |
| Scheduling policies for federated learning in wireless networks: An overview W SHI, Y SUN, X HUANG, S ZHOU, Z NIU ZTE Communications 18 (2), 11-19, 2020 | 5 | 2020 |
| Adaptive transmission for edge learning via training loss estimation X Huang, S Zhou ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 5 | 2020 |
| Latency guaranteed edge inference via dynamic compression ratio selection X Huang, S Zhou 2020 IEEE Wireless Communications and Networking Conference (WCNC), 1-6, 2020 | 5 | 2020 |
| Importance-aware message exchange and prediction for multi-agent reinforcement learning X Huang, S Zhou GLOBECOM 2022-2022 IEEE Global Communications Conference, 6493-6498, 2022 | 3 | 2022 |
| Importance-Aware Data Pre-Processing and Device Scheduling for Multi-Channel Edge Learning X Huang, S Zhou Journal of Communications and Information Networks 7 (4), 394-407, 2022 | 1 | 2022 |
| QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning X Huang, S Zhou IEEE Transactions on Mobile Computing, 2023 | | 2023 |
| COMMUNICATION TECHNOLOGIES FOR EFFICIENT EDGE LEARNING D Gündüz, DB Kurka, M Jankowski, MM Amiri, E Ozfatura, S Sreekumar, ... IEEE Communications Magazine, 2, 2020 | | 2020 |
| 18 Timely Wireless Edge Inference S Zhou, W Shi, X Huang, Z Niu | | |
