The project aims to evaluate the vulnerability of Federated Learning systems to targeted data poisoning attack known as Label Flipping Attack. The project studies the scenario that a malicious participant can only manipulate the raw training data on their device. Hence, non-expert malicious participants can achieve poisoning without knowing the model type, the parameters, and the Federated Learning process. In addition, the project also analyses the possibility and effectiveness of concealing the tracks while poisoning the raw data of other devices.