Knowledge distillation has been known as a useful way for model compression. It has been recently adopted in the distributed training domain, such as federated learning, as a way to transfer knowledge between already pre-trained models. Knowledge distillation in distributed settings promises advantages, including significantly reducing the communication overhead and allowing heterogeneous model architectures. However, distillation is still not well studied and understood in such settings, which hinders the possible gains. We bridge this gap by performing an experimental analysis of the distillation process in the distributed training setting, mainly with non-IID data. We highlight some elements that require special considerations when transferring knowledge between already pre-trained models: the transfer set, the temperature, the weight, and the positioning. Appropriately tuning these hyper-parameters can remarkably boost learning outcomes. In our experiments, around two-thirds of the participants require settings other than commonly used default settings in literature, and appropriate tuning can reach more than five times improvement on average.