37 citations · 171 across the 17 of their papers we have counts for
6 papers · 1 filter
MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs
Zhenyan Lu, Daliang Xu, Dongqi Cai +5
Large language models (LLMs) are deployed on mobile devices to power killer applications such as intelligent assistants. LLMs pre-trained on general corpora often hallucinate when…
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts
Hanzi Mei, Dongqi Cai, Ao Zhou +2
As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for data is growing. Much of this data is private and distributed across edge devices, making F…
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
Zeling Zhang, Dongqi Cai, Yiran Zhang +3
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA,…
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Mengwei Xu, Wangsong Yin, Dongqi Cai +15
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine lear…
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…
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…