DP-FedAdamW delivers an unbiased second-moment estimator for AdamW in DPFL, proving linear convergence acceleration without heterogeneity assumptions and outperforming SOTA by 5.83% on Tiny-ImageNet with Swin-Base at ε=1.
Dual defense: Enhancing privacy and mitigating poison- ing attacks in federated learning.Advances in Neural Infor- mation Processing Systems, 37:70476–70498, 2024
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DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models
DP-FedAdamW delivers an unbiased second-moment estimator for AdamW in DPFL, proving linear convergence acceleration without heterogeneity assumptions and outperforming SOTA by 5.83% on Tiny-ImageNet with Swin-Base at ε=1.