185 citations · 445 across the 25 of their papers we have counts for
25 papers
Federated Few-Shot Learning for Mobile NLP
Dongqi Cai, Shangguang Wang, Yaozong Wu +2
Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy…
Towards Practical Few-shot Federated NLP
Dongqi Cai, Yaozong Wu, Haitao Yuan +3
Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often…
Efficient NLP Model Finetuning via Multistage Data Filtering
Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin +1
As model finetuning is central to the modern NLP, we set to maximize its efficiency. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we explo…
STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining
Liwei Guo, Wonkyo Choe, Felix Xiaozhu Lin
Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy…
Protecting File Activities via Deception for ARM TrustZone
Liwei Guo, Kaiyang Zhao, Yiying Zhang +1
A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesyste…
FedAdapter: Efficient Federated Learning for Modern NLP
Dongqi Cai, Yaozong Wu, Shangguang Wang +2
Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private dat…