As the size of machine learning models scales up, conventional federated learning (FL) encounters challenges on wireless edge clients due to scarce resources. Split federated learning (SFL) mitigates these constraints by offloading part of the model to the edge server. However, most prior work adopts two-tier synchrono...
Chun-Feng Xie, Zhi-Xiong Chen, Wen-Qiang Yi et al.· IEEE International Symposium...· 0 citations
Wireless multimodal federated learning (MFL) is promising for privacy-preserving edge intelligence, but its practical deployment is challenged by modality heterogeneity, client resource heterogeneity, and the high communication and computation cost of multimodal models. This paper proposes a beamforming-aided wireless...
Chun-Feng Xie, Zhi-Xiong Chen, Wenqiang Yi et al.· 2026 IEEE/CIC International...· 0 citations
With the growing number of antennas in massive multiple-input multiple-output (MIMO) systems, robust and fast channel estimation becomes increasingly critical yet remains highly challenging. In this work, we propose a lightweight zero-shot self-supervised (ZS-SS) learning framework. It leverages non-local self-similari...
Zijun Gao, Wenqiang Yi, Fatma Benkhelifa et al.· IEEE Transactions on Wireles...· 0 citations
The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burden...
Chunfeng Xie, Zhixiong Chen, Wenqiang Yi et al.· IEEE Transactions on Communi...· 1 citation
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