Decentralized Federated Learning (DFL) is a new paradigm that arose to address the problems of having a central server in Federated Learning (FL). FL allows to train ML models in a privacy preserving way by sending model updates instead of the raw data to a central server for aggregation, but it presents issues with single point of failure and communication bottleneck at the server. DFL instead eliminates the need for a central server by exchanging the model updates directly between the participating clients. In order to reduce resource consumption, a small number of connections among the clients is preferable. However, the topology of the system plays an important role in the performance of the training task, especially under heterogeneous data distribution, and the elimination of connections can deteriorate it severely.