@Article{cmc.2026.077222,
AUTHOR = {Abuzar Khan, Abid Iqbal, Ghassan Husnain, Fahad Masood, Mohammed Al-Naeem, Sajid Iqbal},
TITLE = {Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n3/66955},
ISSN = {1546-2226},
ABSTRACT = {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 with cross-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-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic non-IID client partitions, our evaluation shows that non-private FedAvg attains validation AUC <mml:math id="mml-ieqn-1"><mml:mo>≈</mml:mo></mml:math> 0.90 and test AUC <mml:math id="mml-ieqn-2"><mml:mo>≈</mml:mo></mml:math> 0.78, closely matching a centralized baseline. Moderate differential privacy noise (<mml:math id="mml-ieqn-3"><mml:mi>δ</mml:mi><mml:mo>≤</mml:mo></mml:math> 0.5) preserves most of this utility across KRUM and trimmed-mean aggregation. Communication analysis indicates that secure aggregation introduces negligible overhead compared with 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 <mml:math id="mml-ieqn-4"><mml:mo>≈</mml:mo></mml:math> 0.51 with no correctly inferred training members. Overall, the findings show that effective, regulation-conscious maritime analytics can be achieved without centralizing raw AIS data, offering a practical pathway for deploying resilient, privacy-enhanced AI services in distributed maritime environments.},
DOI = {10.32604/cmc.2026.077222}
}