As they are based on a distributed network, FL applications have to address risks of potential attacks or failures of numerous workers. Attacks on a federated learning setting could take different forms. An attack could originate from a participant by altering the data used to train the ML model, or altering the model itself, with the potential to compromise the global model. It can also be an attempt by a participant or the server to infer data from other workers through the model updates it receives. Failures are, in contrast, of non-malicious nature but can also adversely affect the performance of the FL process. Network unreliability, the limited availability, unresponsiveness or drop-out of the workers are more prevalent problems in a distributed setting, and FL implementations should be designed to be robust to these threats.