Shengli Liu

Shengli Liu

Shanghai University

Research

Edge Intelligence

Theoretical analysis and joint learning and communication algorithms for collaborative learning system with constrained and heterogeneous resources, such as traditional federated learning and decentralized learning.

  • The theoretical analysis includes convergence rate (average norm of gradient, model divergence, etc.) and learning cost (latency, energy consumption, etc.).
  • The joint learning and communication algorithms includes device selection, model pruning, topology optimization, user association, etc.

Publications

  • S. Liu, G. Yu, X. Chen and M. Bennis, “Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning With IID and Non-IID Data,” in IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 7852-7866, Oct. 2022.
  • S. Liu, G. Yu, R. Yin, J. Yuan, L. Shen and C. Liu, “Joint Model Pruning and Device Selection for Communication-Efficient Federated Edge Learning,” in IEEE Transactions on Communications, vol. 70, no. 1, pp. 231-244, Jan. 2022.
  • S. Liu, G. Yu, R. Yin and J. Yuan, “Adaptive Network Pruning for Wireless Federated Learning,” in IEEE Wireless Communications Letters, vol. 10, no. 7, pp. 1572-1576, July 2021.
  • S. Liu, G. Yu, R. Yin, J. Yuan and F. Qu, “Communication and Computation Efficient Federated Learning for Internet of Vehicles With a Constrained Latency,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 1038-1052, Jan. 2024.
  • S. Liu, G. Yu, D. Wen, X. Chen, M. Bennis and H. Chen, “Communication and Energy Efficient Decentralized Learning Over D2D Networks,” in IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 9549-9563, Dec. 2023.
  • C. Chen, B. Jiang, S. Liu, C. Li, C. Wu and R. Yin, “Efficient Federated Learning in Resource-Constrained Edge Intelligence Networks Using Model Compression,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2643-2655, Feb. 2024.
  • X. Xu, G. Yu and S. Liu, “Adaptive Modulation for Wireless Federated Edge Learning,” in IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 4, pp. 1096-1109, Aug. 2023.
  • G. Ding, S. Liu, J. Yuan and G. Yu, “Joint URLLC Traffic Scheduling and Resource Allocation for Semantic Communication Systems,” in IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7278-7290, July 2024.
  • Y. Chen and S. Liu, “Joint Mode Selection and Resource Allocation for D2D-Assisted Wireless Federated Learning,” in IEEE Wireless Communications Letters, 2024.
  • S. Liu, Y. Shen, J. Yuan, C. Wu and R. Yin, “Storage-Aware Joint User Scheduling and Bandwidth Allocation for Federated Edge Learning,” in IEEE Transactions on Cognitive Communications and Networking, 2024.

Educations

  • 2024.07 - (now), Associate Professor, School of Communication and Information Engineering, Shanghai University.
  • 2022.07 - 2024.06, Postdoctoral Researcher, College of Information Science and Electronic Engineering, Zhejiang University.
  • 2017.09 - 2022.06, PhD, College of Information Science and Electronic Engineering, Zhejiang University.