Linh Tran
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126
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Crypto×1
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2026
Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
The paper proposes FedPower, a novel differentially private cross-silo Federated Learning framework that uses PowerDP to reconstruct and project client updates into a secure low-rank space, effectively mitigating the negative impact of DP noise on parameter-efficient fine-tuning.
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