Federated Learning (FL) is a recent technique that emerges in order to handle the huge amount of training data needed in machine learning algorithms and the concern of privacy challenges in such models. Simultaneously, the field of Quantum Computing (QC) has grown exponentially and quantum properties such as entanglement and superposition had demonstrated to be more efficient in certain machine learning tasks, given raise to the field known as Quantum Machine Learning (QML). Thus, a handful of articles have recently studied a possible Quantum Federated Learning (QFL) framework. This paper presents an exhaustive search on this topic and aims to fill the gap in the literature in a comprehensive way. Moreover, it offers an original taxonomy of the field and proposes future challenges and remarks.