Papers by Yuji Byun
Towards Federated Low-Rank Adaptation of Language Models with Rank Heterogeneity (2025.naacl-short)
Copied to clipboard
| Challenge: | Low-rank adaptation (LoRA) is an efficient alternative to full-weight adaptation in federated fine-tuning of language models, significantly reducing computational costs. |
| Approach: | They propose a low-rank adaptation method that freezes original weights and trains only the update parametrized as a product of two low-ranked matrices. |
| Outcome: | The proposed method accelerates convergence and enhances the global model’s predictive performance. |