8 citations · 35 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective
Tianyang Duan, Zongyuan Zhang, Zheng Lin +7
Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for eva…
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…
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…