Omid Orang

One-Shot Federated Class-Incremental Learning for Medical Imaging via Variational Feature Transfer

A novel class-incremental continual learning model for a one-shot FL paradigm, in which each task introduces new classes, clients observe heterogeneous and evolving class distributions, and communication with the server occurs only once, substantially mitigates catastrophic forgetting while consistently enhancing recognition of newly introduced classes.