Abstract: A computer-implemented method, a computer program product, and a computer system for supervised anomaly detection in federated learning. A server in a federated learning system generates a training dataset including malicious data samples and benign data samples. The server trains update-generating models on the malicious data samples and the benign data samples in the training dataset. The server generates benign model updates and malicious model updates, through training the update-generating models. The server trains an anomaly detector on the malicious model updates and the benign model updates. The server deploys the anomaly detector to the federated learning system, for supervised anomaly detection in the federated learning system.