Projection-Based Federated Learning on Compact Submanifolds with Geometry-Independent Differential Privacy

Federated learning trains a shared model across clients without exchanging their data, yet the updates they transmit still leak information about individual records. Enforcing differential privacy is harder still when the model parameters lie on a Riemannian manifold. Existing methods rebuild the no