Federated Learning is a Machine Learning solution that trains a global model by aggregating weights from different peers. Federated Learning does not require that data be shared among nodes; however, it is not exempt from privacy and/or security issues. This systematic review focuses on the major security and privacy threats related to the definition and implementation of Federated Learning frameworks. This study aims to provide a comprehensive analysis of potential adversary cyber attacks throughout the execution of Federated Learning, in order to characterize and classify Federated Learning protocols capable of addressing critical robustness concerns—including privacy-preserving techniques, local data protection, efficiency, and accuracy—while highlighting the critical points that remain to be addressed.