Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behaviour. Additionally, federated learning has provided a way for a global model to be trained with multiple clients” data without requiring the client to directly share their data. This paper proposes a novel anomaly detector via federated learning to detect malicious network activity on a client's server. In our experiments., we use an autoencoder with a classifier in a federated learning framework to determine if the network activity is benign or malicious. By using FedSam., our novel min-max scalar and sampling technique., we created a federated learning framework that allows the global model to learn from heterogeneous clients and., in turn., provide a means for each client to improve their intrusion detection system's defense against cyber-attacks.