Backdoor attacks pose a severe threat to federated graph learning (FGL), where malicious clients can inject hidden triggers into the global model without being detected. Defending against such attacks is particularly challenging due to the complex graph structures and the stealthy nature of trigger patterns. In this work, we propose MultiKD, a novel backdoor mitigation method based on attention-guided multi-teacher distillation. Unlike existing defenses that focus on detecting suspicious clients or restricting backdoor activation, MultiKD directly purifies the global model on the server side by exploiting intermediate representations. It integrates knowledge from multiple client models and guides the global model to suppress backdoor behaviors by aligning attention maps and preserving inter-layer relational consistency. Our defensive intuition enables MultiKD to retain task-relevant information while mitigating malicious patterns, even when some teacher models are compromised. Extensive experiments on four real-world datasets demonstrate the effectiveness of our approach in significantly reducing attack success rate ($\leq$ 8\%) with minimal impact on utility ($\leq$ 5\%).