Author

Yulong Shen

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Preprint Aug 2026

From Bilinear to Linear: Differentially Private Federated LoRA via Low-Dimensional Parameterization

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. Differential privacy (DP) mitigates...

Le-Le Zheng, Rui Hu, Tao Zhang et al. · 0 citations

Preprint Aug 2026

FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA

FedGSA, a geometry-consistent aggregation framework for differentially private federated LoRA, is proposed and it is proved that FedGSA incurs no additional privacy loss beyond client-side DP training and establishes its convergence under standard assumptions.

Le-Le Zheng, Rui Hu, Tao Zhang et al. · 0 citations