Heterogeneous federated learning has emerged as a prominent research focus in recent years. Various methods have been developed to address data heterogeneity, while personalized federated learning has made strides in tackling model heterogeneity. However, these approaches often incur additional computational and communication costs. Federated distillation, a federated learning paradigm capable of addressing heterogeneity issues while reducing communication overhead, is gaining substantial attention. Nevertheless, current federated distillation methods do not differentiate the quality of logits uploaded by different clients. A large number of incorrect logits can compromise the accuracy of the final teacher logits, inevitably impacting the overall effectiveness of federated distillation. To address this, we propose a contribution-based federated distillation knowledge aggregation scheme—CoFD. This method assigns different aggregation weights to logits from different clients, resulting in more effective teacher logits to guide the distillation process for other clients’ models. Specifically, we model each client’s performance on each piece of public data in each round as a Multi-Armed Bandit problem and use the results of Thompson sampling as aggregation weights. Experimental results demonstrate that our method is nearly optimal compared to existing federated distillation methods. Our code is available at https://github.com/AlexWaker/CoFD .