TARGETED FORGETTING: A FRAMEWORK FOR CLIENT- CLASS CENTRIC KNOWLEDGE REMOVAL IN FEDERATED LEARNING

Federated unlearning (FU) aims to remove the influence of specific clients or classes from a trained global model upon request, without accessing raw data or degrading overall performance. Existing FU methods often struggle to fully eliminate targeted information in a single unlearning round, particularly under non-IID data distributions, and may adversely affect non-target performance. In this work, we propose a hybrid federated unlearning framework that supports client-level, class-level, and client-class unlearning through parameter-level updates. It combines server-side gradient residual correction (proxy filtering) with client-side perturbation-based pruning (weight negation) to precisely remove targeted influences without requiring access to local training data. This design enables efficient, data-independent unlearning and generalizes across IID, non-IID, and label-shifted settings. Experiments on MNIST, FMNIST, and CIFAR10 demonstrate that our framework achieves effective single-client, multi-client, single-class, and multi-class unlearning with minimal impact on non-target accuracy, while maintaining scalability and robustness across federated rounds.