  Machine learning has helped advance the field of anomaly detection by
incorporating classifiers and autoencoders to decipher between normal and
anomalous behavior. 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 our novel min-max scalar and sampling technique, called
FedSam, we determined federated learning allows the global model to learn from
each client's data and, in turn, provide a means for each client to improve
their intrusion detection system's defense against cyber-attacks.
