{"ID":2833397,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03584","arxiv_id":"2512.03584","title":"Federated Learning and Trajectory Compression for Enhanced AIS Coverage","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.03584","url_pdf":"https://arxiv.org/pdf/2512.03584v1","authors":"[\"Thomas Gräupl\",\"Andreas Reisenbauer\",\"Marcel Hecko\",\"Anil Rasouli\",\"Anita Graser\",\"Melitta Dragaschnig\",\"Axel Weissenfeld\",\"Gilles Dejaegere\",\"Mahmoud Sakr\"]","published":"2025-12-03T09:10:07Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}