Author

Yulong Shen

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Preprint Aug 2026

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

FedHSIP reformulates all LoRA parameters into a shared low-dimensional trainable vector, enabling clients to optimize and communicate only low-dimensional updates, and transforms federated LoRA from a bilinear factor aggregation problem into a unified linear parameter space, thereby eliminating aggregation mismatch and...

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