# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 2 Federated Learning  
### Description  
FL has been proposed as an efficient approach for handling distributed datasets and decoupling model training from requiring direct access to raw training data. In FL, users exploit shared models trained from excessive amounts of local data without needing centralized storage [10]. Here, devices represent clients in a federation, targeting to solve the learning task under the coordination of a central server. Each client maintains a local training dataset and only computes and transmits an update to the server's global model. FL is well suited for applications where training relies on already available data at each client, thus guaranteeing high privacy and security levels, as attacks will only affect individual devices, and not the whole federation [11].  
In Figure 1, FL operation in different maritime scenarios is depicted, in which deep neural networks (DNNs) are trained at edge nodes and then the aggregated information is sent to the central processing node. Afterwards, the edge nodes are informed for the updated global model, and hence they can adapt to various operational scenarios. Also, communication links with varying levels of bandwidth, reliability, and coverage are shown, such as satellite support for long propagation distances and maritime links, including Unmanned Aerial Vehicle (UAV)-aided communications [12]. The depicted application domains include Internet of Ships (IoS), maritime IoT with buoys, and Internet of Underwater Things (IoUWT).  
By adopting FL, the computational load is balanced among various devices, reducing latency, while local data are not sent to the central processing node, maximizing the privacy of the participating entities. As a result, local devices can also exploit the information observed and processed by other learners using the global model. Moreover, contrary to other distributed learning approaches, FL can operate with unbalanced and non-i.i.d. datasets in scenarios where the selected data differ in type and size [9]. This capability of FL increases its applicability to heterogeneous maritime scenarios and its potential in the maritime domain. In practice, FL can be applied considering various ML training modes per distant location, such as fully connected NNs, convolutional NNs, clustering approaches, support vector machines, etc. FL can provide cost- and time-effective solutions in highly demanding applications in the context of sixth-generation (6G) networks, such as autonomous driving, augmented reality, advanced e-health applications, etc. Due to the harsh propagation conditions met in long-distance communications for underwater segments, we consider that IoUWTs are passive during the FL training process, in the sense that Unmanned Surface Vehicles (USVs) are responsible for collecting their data. As a result, USVs convey the data collected by IoUWTs to train local models and participate in the FL loop.  
Figure 1: Federated learning in various maritime scenarios, including Internet of Ships (IoS), maritime Internet of Things (IoT) with buoys, and Internet of Underwater Things (IoUWT).