Jingheng Zheng — accepted AI-conference papers

2024

On the Convergence of Hierarchical Federated Learning with Gradient Quantization and Imperfect Transmission

Haofeng Sun, Hui Tian, Wanli Ni, Jingheng Zheng

ICASSP 2024accepted

To enhance the robustness and convergence of hierarchical federated learning (HFL) in wireless networks with imperfect channel state information (CSI), a quantized HFL (QHFL) framework is proposed. Considering the local training and communication latency, the outage probability of quantized gradient…

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2023

Semi-Federated Learning for Edge Intelligence with Imperfect SIC

Wanli Ni, Jingheng Zheng, Yonina C. Eldar, Changsheng You, Kaibin Huang

ICASSP 2023accepted

In this paper, we propose a semi-federated learning (SemiFL) framework that allows computing-limited clients to collaboratively train a shared model with resource-abundant clients. Specifically, by supporting the coexistence of model-updating and data-offloading, the SemiFL framework enables both ce…

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