Federated Learning for Anomaly Detection in Maritime Movement Data
This work addresses data privacy and efficiency issues in maritime anomaly detection, though it is incremental as it applies existing federated learning techniques to a specific domain.
The paper tackles the problem of anomaly detection in maritime movement data by introducing M3fed, a federated learning solution that reduces communication costs and improves data privacy, achieving comparable model quality to centralized methods.
This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed.