Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning

arXiv:2606. 12679v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an adversarial contributor, auditing per-client influence, or honoring a departed participant's right to be forgotten.