FedPower improves the accuracy-privacy tradeoff in differentially private LoRA-based federated learning by reconstructing and clipping full-rank updates then using PowerDP to inject noise before orthonormalization in low-rank factorization.
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Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
FedPower improves the accuracy-privacy tradeoff in differentially private LoRA-based federated learning by reconstructing and clipping full-rank updates then using PowerDP to inject noise before orthonormalization in low-rank factorization.