Federated Learning and Trajectory Compression for Enhanced AIS Coverage

2025/12/03 by Gräupl, Thomas, Reisenbauer, Andreas, Hecko, Marcel +6
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Maritime Navigation and Safety #Underwater Vehicles and Communication Systems

paper · doi:10.48550/arxiv.2512.03584

openalex publication_date 2025/12/03 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28

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.

Citations

• Federated Learning for Anomaly Detection in Maritime Movement Data
• New algorithms for the simplification of multiple trajectories under bandwidth constraints
• Advances and Open Problems in Federated Learning

Related

• Federated Learning for Anomaly Detection in Maritime Movement Data
• WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory
• Optimizing Vessel Trajectory Compression
• Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey
• AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting