Ke An

FedDRU: Depth-Aware Residual Client Unlearning for Heterogeneous Federated Learning

Client-level federated unlearning seeks to update an already trained global model so that the influence of a specified client is weakened, while the model remains effective for the remaining clients. Existing methods are mostly designed for homogeneous model settings and often rely on retraining, historical updates, or...