3 papers
cs.LG2026
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…
cs.LG2026
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
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
Mikko A. Heikkilä
Federated learning is a distributed learning setting where the main aim is to train machine learning models without having to share raw data but only what is required for learning.…