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cs.LG2025
On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
Aki Rehn, Linzh Zhao, Mikko A. Heikkilä +1
Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for training large models under privac…
cs.LG2025
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
Linzh Zhao, Aki Rehn, Mikko A. Heikkilä +2
Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model pr…