arXiv:2609.26091v1 Announce Type: new Abstract: Federated Low-Rank Adaptation (LoRA) provides an efficient solution for finetuning large language models across distributed and privacy-sensitive data. However, despite avoiding raw data sharing, federated LoRA remains vulnerable to privacy leakage through transmitted model updates.
From Bilinear to Linear: Differentially Private Federated LoRA via Low-Dimensional Parameterization
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