Proactive Unlearning Readiness: A Two-Phase Framework for Efficient Federated Unlearning

Federated learning (FL) is widely used in privacy-sensitive applications, including medical imaging, recommendation, and personalized modeling. Once deployed, these systems may receive deletion requests that ask the model to forget the influence of specific clients, classes, or samples; in FL, this requirement is typically handled through federated unlearning (FU). Existing FU methods are mostly reactive and often depend on partial retraining or expensive parameter corrections after a request arrives. We propose Efficient and Reversible Proactive Federated Unlearning (ERPFU), a proactive two-phase framework that addresses this bottleneck from both training and unlearning. During training, weight orthogonality regularization (WOR) reduces interference among learned filters and prepares the model for localized future updates. During unlearning, a sparse classifier adapter (SCA) freezes the pretrained backbone and overwrites target knowledge through a small set of classifier parameters, reducing cost while keeping the update path controlled. ERPFU is intended as an efficient approximation to retraining-based unlearning, not as a claim of complete encoder scrubbing. Experiments on FashionMNIST, CIFAR-10, and CIFAR-100, covering client-, class-, and sample-level forgetting, show that ERPFU stays close to retraining in retention while using much less computation and communication. Additional analyses on non-IID partitions, orthogonality strength, and sparsity show that the method remains stable in heterogeneous FL settings.