Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence

Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance withcross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-designedge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIStrajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employingsecure and robust aggregation. Using a public AIS corpus with realistic non-IID client partitions, our evaluationshows that non-private FedAvg attains validation AUC ≈ 0.90 and test AUC ≈ 0.78, closely matching a centralizedbaseline. Moderate differential privacy noise (δ ≤ 0.5) preserves most of this utility across KRUM and trimmedmean aggregation. Communication analysis indicates that secure aggregation introduces negligible overhead comparedwith standard FedAvg, while homomorphic encryption increases payload size by roughly an order of magnitude.Membership-inference experiments further demonstrate strong privacy protection, yielding ROC AUC ≈ 0.51 withno correctly inferred training members. Overall, the findings show that effective, regulation-conscious maritimeanalytics can be achieved without centralizing raw AIS data, offering a practical pathway for deploying resilient,privacy-enhanced AI services in distributed maritime environments.