9 citations · 16 across the 18 of their papers we have counts for
18 papers
Beyond Scaling: Agents Are Heading to the Edge
Chunlin Tian, Dongqi Cai, Wanru Zhao +1
The bottleneck of useful agentic intelligence has shifted from compressing world knowledge into a single model to executing a coordinated system. This position paper argues that pe…
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…
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…
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…
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks
Shichen Zhan, Yebo Wu, Chunlin Tian +2
Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumpt…
Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing
Wu Yebo, Li Li, Tian Chunlin +4
In this paper, we propose SmartFreeze, a framework that effectively reduces the memory footprint by conducting the training in a progressive manner. Instead of updating the full mo…
Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
Yebo Wu, Jingguang Li, Chunlin Tian +3
Federated Learning (FL) enables multiple clients to collaboratively train a shared model while preserving data privacy. However, the high memory demand during model training severe…
When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models
Haicheng Liao, Yongkang Li, Chengyue Wang +6
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditio…
CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions
Haicheng Liao, Haoyu Sun, Huanming Shen +6
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substan…
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
Chunlin Tian, Zhan Shi, Zhijiang Guo +2
Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such a…
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin +2
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round…
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Zhiyuan Ning, Chunlin Tian, Meng Xiao +5
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Tr…