8 citations · 23 across the 11 of their papers we have counts for
11 papers
Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents
Yuxin Zhang, Mengxue Hu, Zheng Lin +8
Large language model (LLM) agents excel at solving complex long-horizon tasks through autonomous interaction with environments. However, their real-world deployment faces a fundame…
SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacy
Zhuosen Bao, Xia Du, Zheng Lin +8
With the deep integration of facial recognition into online banking, identity verification, and other networked services, achieving effective decoupling of identity information fro…
Enabling Near-realtime Remote Sensing via Satellite-Ground Collaboration of Large Vision-Language Models
Zihan Li, Jiahao Yang, Yuxin Zhang +2
Large vision-language models (LVLMs) have recently demonstrated great potential in remote sensing (RS) tasks (e.g., disaster monitoring) conducted by low Earth orbit (LEO) satellit…
SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression
Zehang Lin, Zheng Lin, Miao Yang +7
The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated l…
A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation
Yuxin Zhang, Jiahao Yang, Zhe Chen +3
Recently, large vision-language models (LVLMs) unleash powerful analysis capabilities for low Earth orbit (LEO) satellite Earth observation images in the data center. However, fast…
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…
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…
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…
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Yuxin Zhang, Zheng Lin, Zhe Chen +5
Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model conve…
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Xuanjie Hu, Yuxin Zhang +6
The scalability of large language models (LLMs) in handling high-complexity models and large-scale datasets has led to tremendous successes in pivotal domains. While there is an ur…
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
Yuxin Zhang, Haoyu Chen, Zheng Lin +2
Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for…