25 citations · 63 across the 37 of their papers we have counts for
16 papers · 1 filter
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
Wenhao Wang, Xiaoyu Liang, Rui Ye +3
The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data. However, this practice raises privacy concerns due to the memorizat…
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…
Data Quality Control in Federated Instruction-tuning of Large Language Models
Yaxin Du, Rui Ye, Fengting Yuchi +4
Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models (LLMs) by leveraging massively distributed data. However, the decentral…
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
Rui Ye, Rui Ge, Yuchi Fengting +3
Federated instruction tuning enables multiple clients to collaboratively fine-tune a shared large language model (LLM) that can follow humans' instructions without directly sharing…
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
Zezhou Wang, Yaxin Du, Xingjun Ma +3
Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data…
Self-Localized Collaborative Perception
Zhenyang Ni, Zixing Lei, Yifan Lu +4
Collaborative perception has garnered considerable attention due to its capacity to address several inherent challenges in single-agent perception, including occlusion and out-of-r…
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu +3
Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing. Ideally, by training on decentr…
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Rui Ye, Rui Ge, Xinyu Zhu +5
Federated learning has enabled multiple parties to collaboratively train large language models without directly sharing their data (FedLLM). Following this training paradigm, the c…
Robust Collaborative Perception without External Localization and Clock Devices
Zixing Lei, Zhenyang Ni, Ruize Han +5
A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information excha…
Language-Driven Interactive Traffic Trajectory Generation
Junkai Xia, Chenxin Xu, Qingyao Xu +3
Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic particip…
Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models
Zhenyang Ni, Rui Ye, Yuxi Wei +3
Vision-Large-Language-models(VLMs) have great application prospects in autonomous driving. Despite the ability of VLMs to comprehend and make decisions in complex scenarios, their…
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
Wanru Zhao, Yaxin Du, Nicholas Donald Lane +2
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further s…
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Rui Ye, Wenhao Wang, Jingyi Chai +6
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performan…
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Shaoheng Fang, Zuhong Liu, Mingyu Wang +3
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly…
Learn What You Need in Personalized Federated Learning
Kexin Lv, Rui Ye, Xiaolin Huang +2
Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parame…
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
Shaoheng Fang, Rui Ye, Wenhao Wang +5
Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single R…