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.

翻译：本文提出了VesselEdge系统，该系统利用联邦学习和带宽受限的轨迹压缩技术，通过扩展AIS覆盖范围来增强海上态势感知能力。VesselEdge将船舶转变为移动传感器，支持在低带宽连接下实现实时异常检测和高效数据传输。该系统集成了用于联邦学习的M3fed模型和用于轨迹压缩的BWC-DR-A算法，并优先处理异常数据。初步结果表明，利用历史数据，VesselEdge在提升AIS覆盖范围和态势感知方面具有显著效果。