Federated Learning and Trajectory Compression for Enhanced AIS Coverage

Thomas Gräupl, Andreas Reisenbauer, Marcel Hecko, Anil Rasouli, Anita Graser +4 more
12/3/2025
cs.LG

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Abstract

This paper presents the VesselEdge system, which leverages federated learning and bandwidth-constrained trajectory compression to enhance maritime situational awareness by extending AIS coverage. VesselEdge transforms vessels into mobile sensors, enabling real-time anomaly detection and efficient data transmission over low-bandwidth connections. The system integrates the M3fed model for federated learning and the BWC-DR-A algorithm for trajectory compression, prioritizing anomalous data. Preliminary results demonstrate the effectiveness of VesselEdge in improving AIS coverage and situational awareness using historical data.

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Cite this paper

@article{grupl2025federated,
  title  = {Federated Learning and Trajectory Compression for Enhanced AIS Coverage},
  author = {Thomas Gräupl and Andreas Reisenbauer and Marcel Hecko and Anil Rasouli and Anita Graser and Melitta Dragaschnig and Axel Weissenfeld and Gilles Dejaegere and Mahmoud Sakr},
  year   = {2025},
  eprint = {2512.03584},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.03584v1}
}

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