9 citations · 16 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
Yebo Wu, Jingguang Li, Chunlin Tian +2
Federated fine-tuning enables privacy-preserving Large Language Model (LLM) adaptation, but its high memory cost limits participation from resource-constrained devices. We propose…
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…
RAP: Runtime Adaptive Pruning for LLM Inference
Huanrong Liu, Chunlin Tian, Xuyang Wei +2
Large language models (LLMs) excel at language understanding and generation, but their enormous computational and memory requirements hinder deployment. Compression offers a potent…
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…
AsymLoRA: Harmonizing Data Conflicts and Commonalities in MLLMs
Xuyang Wei, Chunlin Tian, Li Li
Effective instruction fine-tuning on diverse image-text datasets is crucial for developing a versatile Multimodal Large Language Model (MLLM), where dataset composition dictates th…