9 citations · 17 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
Kahou Tam, Kewei Xu, Li Li +1
Federated Learning (FL) has evolved as a powerful tool for collaborative model training across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and…
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…
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training
Yebo Wu, Li Li, Chengzhong Xu
This paper presents ProFL, a new framework that effectively addresses the memory constraints in FL. Rather than updating the full model during local training, ProFL partitions the…