2 citations · 4 across the 4 of their papers we have counts for
7 papers
Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
Yebo Wu, Jingguang Li, Chunlin Tian +3
Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs' high memory demands and edge devices' limited capacity.…
Floe: Federated Specialization for Real-Time LLM-SLM Inference
Chunlin Tian, Kahou Tam, Yebo Wu +4
Deploying large language models (LLMs) in real-time systems remains challenging due to their substantial computational demands and privacy concerns. We propose Floe, a hybrid feder…
FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management
Kahou Tam, Chunlin Tian, Li Li +2
Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundame…
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge
Chunlin Tian, Xinpeng Qin, Kahou Tam +5
Deploying large language models (LLMs) on edge devices is crucial for delivering fast responses and ensuring data privacy. However, the limited storage, weight, and power of edge d…
A Survey on Federated Fine-tuning of Large Language Models
Yebo Wu, Chunlin Tian, Jingguang Li +8
Large Language Models (LLMs) have demonstrated impressive success across various tasks. Integrating LLMs with Federated Learning (FL), a paradigm known as FedLLM, offers a promisin…
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
Chunlin Tian, Li Li, Kahou Tam +2
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory…