Yongle Li

Yongle Li, Bo Liu, Sheng Huang et al.

In federated learning, fine-tuning pre-trained foundation models poses significant challenges, particularly regarding high communication cost and suboptimal model performance due to data heterogeneity between the clients. To address these issues, this paper introduces communication-efficient federated LoRA adaption (CE-LoRA), a method that employs a tri-factorization low-rank adaptation approach with personalized model parameter aggregation. We first presents a novel LoRA parameter factorization by introducing a small-size dense matrix, which can significantly reduce the communication cost and achieve comparable empirical performance than transferring the low-rank parameter matrix used by existing methods. Without violating data privacy, the server considers the client similarity in both training dataset and model parameter space, and learns personalized weights for model aggregation. Our experiments on various LLM and VLM fine-tuning tasks demonstrate that CE-LoRA not only significantly reduces communication overhead but also improves performance under not independently and identically distributed data conditions. In addition, CE-LoRA improves data privacy protection, effectively mitigating gradient-based data reconstruction attacks.

Muchen Yang, Moxin Li, Yongle Li et al.

Gradient-free prompt optimization methods have made significant strides in enhancing the performance of closed-source Large Language Models (LLMs) across a wide range of tasks. However, existing approaches make light of the importance of high-quality prompt initialization and the identification of effective optimization directions, thus resulting in substantial optimization steps to obtain satisfactory performance. In this light, we aim to accelerate prompt optimization process to tackle the challenge of low convergence rate. We propose a dual-phase approach which starts with generating high-quality initial prompts by adopting a well-designed meta-instruction to delve into task-specific information, and iteratively optimize the prompts at the sentence level, leveraging previous tuning experience to expand prompt candidates and accept effective ones. Extensive experiments on eight datasets demonstrate the effectiveness of our proposed method, achieving a consistent accuracy gain over baselines with less than five optimization steps.