2 citations · 3 across the 4 of their papers we have counts for
8 papers
AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
Zhijie Cai, Haolong Chen, Guangxu Zhu
Fine-tuning LLMs is necessary for various dedicated downstream tasks, but classic backpropagation-based fine-tuning methods require substantial GPU memory. To this end, a recent wo…
Three Birds, One Stone: Solving the Communication-Memory-Privacy Trilemma in LLM Fine-tuning Over Wireless Networks with Zeroth-Order Optimization
Zhijie Cai, Yuhao Zheng, Haolong Chen +3
Federated Learning (FL) offers a promising pathway for collaboratively fine-tuning Large Language Models (LLMs) at the edge; however, this paradigm faces a critical bottleneck: the…
A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
Xiaojie Li, Zhijie Cai, Nan Qi +5
The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC fo…
KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
Zijian Zhao, Zhijie Cai, Tingwei Chen +4
Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information…
LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking
Zijian Zhao, Tingwei Chen, Fanyi Meng +4
Data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing dat…
FeedSign: Robust Full-parameter Federated Fine-tuning of Large Models with Extremely Low Communication Overhead of One Bit
Zhijie Cai, Haolong Chen, Guangxu Zhu
Federated fine-tuning (FFT) attempts to fine-tune a pre-trained model with private data from distributed clients by exchanging models rather than data under the orchestration of a…