DeRe: Controlling Update Reintroduction in Cross-Domain Federated Unlearning

Federated unlearning (FU) aims to remove requested training effects from a federated model without retraining from scratch. Some FU requests go beyond excluding data from training and require the resulting global model to have low accuracy on the requested data while preserving retained knowledge. We extend this goal to cross domain FU, a more practical setting where clients may have different input distributions. After unlearning, the global model should suppress the requested classes on the forget client's domain while preserving the same classes on other domains and performance on the remaining data. In this setting, class information from other clients and domain information retained by the forget client may combine during aggregation and restore the target behavior. We refer to this recovery path as Update Reintroduction (UR). Stronger deletion can reduce UR but may also hurt retained accuracy. To address this tension, we propose DeRe (De-Reintroduction) with three stages. Probe Based Mask Selection identifies the edit region using relative deletion responses. Projected Class Level Unlearning coordinates deletion and retained updates at the client and server. Mask Constrained Recovery restores utility outside the selected mask without overwriting the selected edits. Experiments on Digit5, DomainNet, and PACS show that DeRe improves the balance between target suppression and retained accuracy for both single and multi class requests. Code is available at https://anonymous.4open.science/r/wqeimhaikc.