31 citations · 70 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
Zheng Lin, Wei Wei, Zhe Chen +4
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated le…
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…
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Zihan Fang, Zheng Lin, Zhe Chen +3
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks,…
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…
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
Zheng Lin, Zhe Chen, Zihan Fang +3
Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensor…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Zheng Lin, Guanqiao Qu, Qiyuan Chen +3
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, t…