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.
Towards understanding convergence and generalization of adamw.IEEE transactions on pattern analysis and machine intelligence, 46(9):6486–6493, 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.