| Client-edge-cloud hierarchical federated learning L Liu, J Zhang, SH Song, KB Letaief ICC 2020-2020 IEEE international conference on communications (ICC), 1-6, 2020 | 871 | 2020 |
| Hierarchical federated learning with quantization: Convergence analysis and system design L Liu, J Zhang, S Song, KB Letaief IEEE Transactions on Wireless Communications 22 (1), 2-18, 2022 | 134 | 2022 |
| Edge-assisted hierarchical federated learning with non-iid data L Liu, J Zhang, SH Song, KB Letaief arXiv preprint arXiv:1905.06641, 2019 | 93 | 2019 |
| Communication-efficient federated distillation with active data sampling L Liu, J Zhang, SH Song, KB Letaief ICC 2022-IEEE International Conference on Communications, 201-206, 2022 | 35 | 2022 |
| A survey of what to share in federated learning: perspectives on model utility, privacy leakage, and communication efficiency J Shao, Z Li, W Sun, T Zhou, Y Sun, L Liu, Z Lin, J Zhang arXiv preprint arXiv:2307.10655, 2023 | 25 | 2023 |
| The Effect of Quantization in Federated Learning: AR\'enyi Differential Privacy Perspective T Kang, L Liu, H He, J Zhang, SH Song, KB Letaief arXiv preprint arXiv:2405.10096, 2024 | 2 | 2024 |
| Binary Federated Learning with Client-Level Differential Privacy L Liu, J Zhang, S Song, KB Letaief GLOBECOM 2023-2023 IEEE Global Communications Conference, 3849-3854, 2023 | 2 | 2023 |