The goal of 6G is to achieve ubiquitous connectivity in heterogeneous and large-scale networks, and Hierarchical Federated Learning (HiFL) is an effective approach to assist AI technologies at the network edge. However, the “waist -shaped client-channel-cloud” architecture of maritime communication network greatly limits the implementation of hierarchical federated edge learning. Considering the aforementioned limitations, a hierarchical federated data distillation method based on UAV-assisted MEC systems is proposed, called Data Augmentation Distilled Federated Learning (DADFL). Specifically, DADFL reduce the communication process between clients and edge servers to just one round. Each client extracts their private dataset, sends synthesized data to the server, and collectively trains a global model. Building upon this, we propose a Global Optimal Iterative Search Algorithm (GOISA) considering client heterogeneity and resource constraints. GOISA minimizes overall latency by optimizing client heterogeneity, bandwidth allocation, and UAV transmission rates. Simulation experiments demonstrate that our proposed system achieves performance comparable to or even better than traditional federated learning, while reducing communication overhead by three orders of magnitude, greatly improving communication efficiency.

Maritime Internet of Things (IoT) with unmanned surface vessels (USVs) faces tight onboard computing and sparse wireless links. Compute-intensive vision and sensing workloads often exceed latency budgets, which undermines timely decisions. In this paper, we propose a novel distributed computation offloading framework for maritime IoT scenarios. By leveraging the limited computational resources of USVs within a device-to-device (D2D)-assisted edge network and the mobility advantages of UAV-assisted edge computing, we design a breadth-first search (BFS)-based distributed computation offloading game. Building upon this, we formulate a global latency minimization problem that jointly optimizes UAV hovering coordinates and arrival times. This problem is solved by decomposing it into subproblems addressed via a joint Alternating Direction Method of Multipliers (ADMM) and Successive Convex Approximation (SCA) approach, effectively reducing the time between UAV arrivals and hovering coordinates. Extensive simulations verify the effectiveness of our framework, demonstrating up to a 49.6% latency reduction compared with traditional offloading schemes.

To enhance the ability of uncrewed surface vessels (USVs) to execute computationally intensive and latency-sensitive tasks in Autonomous Inland Waterway Transportation Systems (AIWTS) under time-varying network topologies and communication conditions, we propose a novel cluster-based distributed computation offloading framework. Specifically, to avoid the increased computational complexity that arises in centralized frameworks as the number of users grows, we design a distributed task offloading game based on exact potential game (EPG) and multi-agent graph reinforcement learning (MAGRL), which aims to maximize overall offloading efficiency by considering dynamically changing device-to-device (D2D) communication quality and cluster topologies. On this basis, to rationally utilize computing and communication resources, we formulate a global delay minimization problem. By designing heuristic algorithms and employing convex optimization methods, we address task allocation and bandwidth allocation problems to achieve global delay minimization. Finally, the simulation results validate that the framework can effectively reduce the safety-critical decision latency by up to 28.6% compared with the traditional offloading mechanism.