Papers by Yongjeong Oh
RB-LoRA: Rank-Balanced Aggregation for Low-Rank Adaptation with Federated Fine-Tuning (2026.findings-eacl)
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| Challenge: | Low-rank adaptation (LoRA) improves fine-tuning of foundation models by updating only compact adapter matrices . varying client device capabilities lead to different adapter ranks, causing rank heterogeneity that undermines aggregation. |
| Approach: | They propose a rank-balanced aggregation framework that decomposes each update into rank-wise components and aligns them using analytically derived weights. |
| Outcome: | Experiments on language and vision models show that RB-LoRA improves under one and three rounds of communication in federated learning environments. |