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.
@article{arxiv.2512.03584,
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},
journal= {arXiv preprint arXiv:2512.03584},
year = {2025}
}