Federated Learning for Anomaly Detection in Maritime Movement Data

Anita Graser, Axel Weißenfeld, Clemens Heistracher, Melitta Dragaschnig, Peter Widhalm
12/4/2025
cs.LG

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Abstract

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.

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Cite this paper

@article{graser2025federated,
  title  = {Federated Learning for Anomaly Detection in Maritime Movement Data},
  author = {Anita Graser and Axel Weißenfeld and Clemens Heistracher and Melitta Dragaschnig and Peter Widhalm},
  year   = {2025},
  eprint = {2512.04635},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.04635v1}
}

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