Federated Learning and Trajectory Compression for Enhanced AIS Coverage

Abstract--This paper presents the V esselEdge system, which leverages federated learning and bandwidth-constrained trajectory compression to enhance maritime situational awareness by extending AIS coverage. V esselEdge transforms vessels into mobile sensors, enabling real-time anomaly detection and efficient data transmission over low-bandwidth connections. The system integrates the M fed model for federated learning and the BWC-DR-A algorithm for trajectory compression, prioritizing anomalous data. Preliminary results demonstrate the effectiveness of V esselEdge in improving AIS coverage and situational awareness using historical data. The Automatic Identification System (AIS) is a tracking system that uses transceivers on ships to monitor marine traffic.