Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices

• URL: http://arxiv.org/abs/2412.20004v1
• Date: Sat, 28 Dec 2024 04:00:42 GMT
• Title: Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
• Authors: Jun Liu, Yunming Liao, Hongli Xu, Yang Xu, Jianchun Liu, Chen Qian,
• Abstract summary: Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner.<n>We propose a novel LoRA-based FedFT framework, termed LEGEND, which faces the difficulty of determining the number of LoRA layers.<n>We analyze the coupled relationship between LoRA depth and rank distribution, and design an efficient LoRA configuration algorithm for heterogeneous devices.
• Score: 24.725928966071212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner. However, there are two critical challenges for efficient FedFT in practical applications, i.e., resource constraints and system heterogeneity. Existing works rely on parameter-efficient fine-tuning methods, e.g., low-rank adaptation (LoRA), but with major limitations. Herein, based on the inherent characteristics of FedFT, we observe that LoRA layers with higher ranks added close to the output help to save resource consumption while achieving comparable fine-tuning performance. Then we propose a novel LoRA-based FedFT framework, termed LEGEND, which faces the difficulty of determining the number of LoRA layers (called, LoRA depth) and the rank of each LoRA layer (called, rank distribution). We analyze the coupled relationship between LoRA depth and rank distribution, and design an efficient LoRA configuration algorithm for heterogeneous devices, thereby promoting fine-tuning efficiency. Extensive experiments are conducted on a physical platform with 80 commercial devices. The results show that LEGEND can achieve a speedup of 1.5-2.8$\times$ and save communication costs by about 42.3% when achieving the target accuracy, compared to the advanced solutions.

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