Selective Aggregation for Low-Rank Adaptation in Federated Learning
Abstract
We investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned $A$ and $B$ matrices. In doing so, we uncover that $A$ matrices are responsible for learning general knowledge, while $B$ matrices focus on capturing client-specific knowledge. Based on this finding, we introduce Federated Share-A Low-Rank Adaptation (FedSA-LoRA), which employs two low-rank trainable matrices $A$ and $B$ to model the weight update, but only $A$ matrices are shared with the server for aggregation. Moreover, we delve into the relationship between the learned $A$ and $B$ matrices in other LoRA variants, such as rsLoRA and VeRA, revealing a consistent pattern. Consequently, we extend our FedSA-LoRA method to these LoRA variants, resulting in FedSA-rsLoRA and FedSA-VeRA. In this way, we establish a general paradigm for integrating LoRA with FL, offering guidance for future work on subsequent LoRA variants combined with FL. Extensive experimental results on natural language understanding and generation tasks demonstrate the effectiveness of the proposed method. Our code is available at https://github.com/Pengxin-Guo/FedSA-LoRA.
Cite
Text
Guo et al. "Selective Aggregation for Low-Rank Adaptation in Federated Learning." International Conference on Learning Representations, 2025.Markdown
[Guo et al. "Selective Aggregation for Low-Rank Adaptation in Federated Learning." International Conference on Learning Representations, 2025.](https://mlanthology.org/iclr/2025/guo2025iclr-selective/)BibTeX
@inproceedings{guo2025iclr-selective,
title = {{Selective Aggregation for Low-Rank Adaptation in Federated Learning}},
author = {Guo, Pengxin and Zeng, Shuang and Wang, Yanran and Fan, Huijie and Wang, Feifei and Qu, Liangqiong},
booktitle = {International Conference on Learning Representations},
year = {2025},
url = {https://mlanthology.org/iclr/2025/guo2025iclr-selective/}
}