Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models

• URL: http://arxiv.org/abs/2602.19926v1
• Date: Mon, 23 Feb 2026 15:05:28 GMT
• Title: Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
• Authors: Jin Liu, Yinbin Miao, Ning Xi, Junkang Liu,
• Abstract summary: Fine-tuning large vision models (LVMs) and large language models (LLMs) under differentially private learning (DPFL) is hindered by a fundamental privacy-utility trade-off.<n>Low-Rank Adaptation (LoRA), a promising parameter-efficient fine-tuning (PEFT) method, reduces computational and communication costs by introducing two trainable low-rank matrices while freezing pre-trained weights.<n>We propose LA-LoRA, a novel approach that decouples gradient interactions and aligns update directions across clients to enhance robustness under stringent privacy constraints.
• Score: 14.755143405057929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large vision models (LVMs) and large language models (LLMs) under differentially private federated learning (DPFL) is hindered by a fundamental privacy-utility trade-off. Low-Rank Adaptation (LoRA), a promising parameter-efficient fine-tuning (PEFT) method, reduces computational and communication costs by introducing two trainable low-rank matrices while freezing pre-trained weights. However, directly applying LoRA in DPFL settings leads to performance degradation, especially in LVMs. Our analysis reveals three previously underexplored challenges: (1) gradient coupling caused by the simultaneous update of two asymmetric low-rank matrices, (2) compounded noise amplification under differential privacy, and (3) sharpness of the global aggregated model in the parameter space. To address these issues, we propose LA-LoRA (\textbf{L}ocal \textbf{A}lternating \textbf{LoRA}), a novel approach that decouples gradient interactions and aligns update directions across clients to enhance robustness under stringent privacy constraints. Theoretically, LA-LoRA strengthens convergence guarantees in noisy federated environments. Extensive experiments demonstrate that LA-LoRA achieves state-of-the-art (SOTA) performance on Swin Transformer and RoBERTa models, showcasing robustness to DP noise and broad applicability across both LVMs and LLMs. For example, when fine-tuning the Swin-B model on the Tiny-ImageNet dataset under a strict privacy budget ($ε= 1$), LA-LoRA outperforms the best baseline, RoLoRA, by 16.83\% in test accuracy. Code is provided in \repolink.

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