DP-FedSOFIM: Second-Order Federated Optimization Under Differential Privacy Without Extra Privacy Cost [R]
Most differentially private federated learning methods are still fundamentally first-order: clip per-example gradients, add Gaussian noise, aggregate, and take a step. DP-FedGD and DP-FedAvg follow this directly. DP-FedA
📄
This source provides headlines only. Use the button below to read the complete article on the original site.
📰 Read the original article on r/MachineLearning
Originally published by r/MachineLearning. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.