Chenchong Du

Privacy-Preserving Federated Learning for Medical Image Classification with Selective Homomorphic Encryption

PASHE-FL offers an empirical privacy–cost trade-off under the stated threat model rather than a formal privacy guarantee, which exploits the structure of personalized federated learning: the client-specific classifier head stays local and is never uploaded, so encryption need only cover the shared backbone.