Mr Lishan Yang
Higher Degree by Research Candidate
School of Computer Science and Information Technology
College of Engineering and Information Technology
| Year | Citation |
|---|---|
| 2025 | Shakeri, A., Zhang, W. E., Beheshti, A., Chen, W., Yang, J., & Yang, L. (2025). FedDPG: An Adaptive Yet Efficient Prompt-Tuning Approach in Federated Learning Settings. In Lecture Notes in Computer Science Vol. 15874 LNCS (pp. 40-51). Sydney, NSW, Australia: Springer Nature Singapore. DOI |
| 2025 | Yang, L., Zhang, W. E., Sheng, Q. Z., Yao, L., Chen, W., & Shakeri, A. (2025). MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning. In Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 3783-3793). ACM. DOI |
| 2024 | Shakeri, A., Chen, P., Shu, Y., Yang, L., Zhang, W. E., & Chen, W. (2024). Transforming Data Product Generation through Federated Learning: An Exploration of FL Applications in Data Ecosystems. In Proceedings of the IEEE International Conference on Web Services, ICWS Vol. 27 (pp. 84-91). Shenzhen, China: IEEE. DOI Scopus1 |
| Year | Citation |
|---|---|
| 2025 | Yang, L., Zhang, W., Sheng, Q. Z., Chen, W., Yao, L., Chen, W., & Shakeri, A. (2025). MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning. |
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