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Open Access

ARTICLE

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

Abuzar Khan1, Abid Iqbal2,*, Ghassan Husnain1,*, Fahad Masood1, Mohammed Al-Naeem3, Sajid Iqbal4
1 Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
2 Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa,Saudi Arabia
3 Department of Computer Networks Communications, CCSIT, King Faisal University, Al Ahsa, Saudi Arabia
4 Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa,Saudi Arabia
* Corresponding Authors: Abid Iqbal. Email: aaiqbal@kfu.edu.sa; Ghassan Husnain. Email: ghassan.husnain@cecos.edu.pk
(This article belongs to the Special Issue: Cloud Computing Security and Privacy: Advanced Technologies and Practical Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077222

Received 04 December 2025; Accepted 19 January 2026; Published online 11 February 2026

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 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.

Keywords

Federated learning; privacy-preserving cloud computing; maritime AIS analytics; differential privacy

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