1 citations · 1 across the 4 of their papers we have counts for
4 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…
Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
Yipeng Liang, Qimei Chen, Guangxu Zhu +2
With the prevalence of Large Learning Models (LLM), Split Federated Learning (SFL), which divides a learning model into server-side and client-side models, has emerged as an appeal…
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Zhonghao Lyu, Yuchen Li, Guangxu Zhu +3
In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fi…