Scalable Marine AIS Architecture with FedMicro for Distributed Vessel Tracking

Abstract

Maritime domain awareness increasingly relies on Automatic Identification System (AIS) data. Yet, traditional monolithic backends struggle to scale with rising message volumes and offer limited resilience, data sovereignty, or rapid deployability. This study addresses the challenges by introducing a novel federated learning and microservices architecture for distributed vessel tracking. Each end node trains local models on a proprietary AIS Maritime environment via Dockerized microservices: Data Pre-Processor, Client Trainer, Aggregator, Global Model Updater, and XAI Service, enabling independent scaling, fault isolation, and federated governance without exposing raw feeds. A global model is aggregated using FedAvg and served with a sub-second latency of 10 ms of aggregation and 406 ms of inference. Experimental evaluation on four benchmark AIS snapshots yields strong predictive performance (MAE = 0.31, RMSE = 0.39, R2 = 0.78) and demonstrates transparent feature attribution via SHAP. These results validate the proposed architecture’s capability to deliver accurate, low-latency energy predictions while preserving data sovereignty and cross-node consistency. This study lays the foundation for robust, interoperable maritime analytics by bridging microservice agility with federated intelligence and XAI.