Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation

TL;DR

Fed-PLoRA is a new federated fine-tuning framework for large language models that effectively handles client heterogeneity by using parallel one-rank adaptation modules, improving accuracy and efficiency.

Contribution

Introduces Fed-PLoRA, a lightweight federated fine-tuning method with parallel one-rank adaptation and a novel folding strategy for heterogeneous client resources.

Findings

Outperforms existing methods in accuracy.

Enhances efficiency in federated fine-tuning.

Addresses heterogeneity challenges effectively.

Abstract

Large Language Models (LLMs) have demonstrated remarkable effectiveness in adapting to downstream tasks through fine-tuning. Federated Learning (FL) extends this capability by enabling collaborative fine-tuning across distributed clients using Low-Rank Adaptation (LoRA), while preserving data privacy by avoiding raw data sharing. However, practical deployments face challenges when clients have heterogeneous resources and thus adopt different LoRA ranks, leading to substantial initialization and aggregation noise that undermines performance. To address these challenges, we propose Fed-PLoRA, a novel lightweight heterogeneous federated fine-tuning (FFT) framework. Fed-PLoRA introduces Parallel One-Rank Adaptation (PLoRA), a new LoRA variant that replaces the classic multi-rank LoRA module with multiple parallel one-rank modules, and a novel Select-N-Fold strategy that folds untrained PLoRA modules into the pre-trained weights before local training, thereby accommodating heterogeneous client resources. We provide a unified analysis of initialization and aggregation noise of Fed-PLoRA and demonstrate how it addresses the limitations of state-of-the-art methods. Extensive experiments on diverse LLM fine-tuning tasks demonstrate that Fed-PLoRA consistently outperforms existing methods in both accuracy and efficiency. The code is available at https://github.com/TNI-playground/Fed-PLoRA.