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20232025
most citedPushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

31 citations · 70 across the 16 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.LG2025

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Zheng Lin, Zhe Chen, Xianhao Chen +2

Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing…

cs.LG2025

HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Zheng Lin, Yuxin Zhang, Zhe Chen +6

Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fi…

cs.NI2025★ 1 cited

SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework

Jiasheng Wu, Jingjing Zhang, Zheng Lin +4

Recently, the rapid development of LEO satellite networks spurs another widespread concern-data processing at satellites. However, achieving efficient computation at LEO satellites…

cs.NI2025

SigChord: Sniffing Wide Non-sparse Multiband Signals for Terrestrial and Non-terrestrial Wireless Networks

Jinbo Peng, Junwen Duan, Zheng Lin +3

While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heav…

cs.NI2025★ 2 cited

Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples

Haoxuan Yuan, Zhe Chen, Zheng Lin +6

Recently, Low Earth Orbit (LEO) satellite networks (i.e., non-terrestrial network (NTN)), such as Starlink, have been successfully deployed to provide broader coverage than terrest…

cs.LG2025★ 2 cited

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data

Yuxin Zhang, Haoyu Chen, Zheng Lin +2

Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL) by organizing edge devices with similar data distribu…

cs.LG2025★ 2 cited

LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks

Zheng Lin, Yuxin Zhang, Zhe Chen +5

Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in de…