RWFUBench: A Cross-Domain Benchmark for Revealing Collateral Damage in Federated Unlearning

Machine unlearning is essential for enforcing data deletion rights and is increasingly demanded in Federated Learning (FL). However, existing studies largely overlook a critical requirement, Unlearning Independence, which requires that uninvolved clients incur no extra unlearning computation or utility loss. This issue is further exacerbated in Cross-Domain Non-IID (CD-NonIID) settings, where clients share the same task and label space but draw data from different sources. Current evaluations, however, predominantly rely on simplified Pseudo-NonIID assumptions. To address this gap, we present a systematic benchmark named RWFUBench for the task of class unlearning in FL under CD-NonIID conditions. Our results reveal a tension between requester target suppression and retaining client utility. Retraining based methods retain high target accuracy consistent with cross-domain knowledge replenishment, while several model editing methods also reduce target accuracy on uninvolved clients. These findings show that aggregate evaluations can overlook collateral damage in federated unlearning. We further provide FedCCCU as a Proof-of-Concept mitigation strategy that reduces collateral damage, supporting future research on Unlearning Independence.