Federated Learning (FL) enables privacy-preserving distributed training but remains vulnerable to backdoor attacks. Attackers can embed malicious trigger-label associations into the global model by participating in the aggregation process. Existing defense methods typically defend against backdoor attacks by detecting and filtering malicious updates that deviate from benign ones. However, we find that these defenses fail under domain skew, where differing feature distributions across clients increase update heterogeneity, making it harder to distinguish malicious updates from benign ones. To address this challenge, we propose $\textbf{DoBlock}$, a novel defense that utilizes an aggregatable domain infuser incapable of embedding malicious associations, through federated training to facilitate cross-domain knowledge sharing. Moreover, DoBlock prevents malicious association propagation by isolating local models from aggregation, as local models remain client-specific and rely solely on local data for training. Experiments on five domain skew datasets (Digits, PACS, VLCS, Office-Caltech10, and DomainNet) show that DoBlock maintains attack success rates below 2.5\%, while achieving the highest main task accuracy, demonstrating superior robustness without sacrificing benign performance.