Selective Aggregation for Low-Rank Adaptation in Federated Learning
Authors: name: Pengxin Guo, name: Shuang Zeng, name: Yanran Wang, name: Huijie Fan, name: Feifei Wang, name: Liangqiong Qu

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.