Papers with General Generation
GMFL: Efficient Global Masking for Federated LLM Fine-tuning (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) has emerged as a prominent solution to mitigate the communication and computation costs in federated fine-tuning of Large Language Models (LLMs). |
| Approach: | They propose a plug-and-play layer freezing mechanism to integrate with existing federated fine-tuning frameworks. |
| Outcome: | The proposed solution reduces communication overhead and lowers computational costs while preserving the performance of the underlying federated fine-tuning methods. |