# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 6 Conclusions  
In this paper, various applications of federated learning in maritime environments were presented. First, the need for maritime federated learning was emphasized together with its benefits in predicting key parameters and detecting faults while safeguarding security and data privacy. In order to highlight the importance of federated learning for maritime transportation systems, an experimental investigation related to fuel consumption prediction was performed, using real datasets from the maritime industry. The results showed that federated learning improved prediction accuracy without incurring a higher complexity than other distributed and centralized learning algorithms. Finally, as a highly promising field in its early stages, various open issues and research directions of maritime federated learning were discussed, aiming to stimulate further interest in the era of 6G-enabled maritime services.  
Conceptualization, A.G. and N.N.; methodology, A.G., P.G. and P.B.; software, A.G.; validation, N.N., P.B. and P.T.; formal analysis, A.G. and P.G.; investigation, P.B.; resources, P.T.; data curation, P.T.; writing--original draft preparation, A.G., P.G., P.B. and N.N.; writing--review and editing, A.K. and P.T.; visualization, A.G. and P.B.; supervision, P.T.; project administration, P.T.; funding acquisition, P.T. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the ICOS (Towards a functional continuum operating system) Project, which is funded by the European Union's HORIZON research and innovation programme under grant agreement No. 101070177 ([[https://www.icos-project.eu/](https://www.icos-project.eu/)]([https://www.icos-project.eu/](https://www.icos-project.eu/)), accessed on 20 June 2024).  
Not applicable.  
Data Availability Statement: The datasets presented in this article are not freely available due to commercial constraints. However, access to the datasets can be provided upon request to the corresponding author.  
The authors would like to thank [PERSON] for his help and Hydrus Engineering S.A. for the data provisioning.  
The authors declare no conflicts of interest. Also, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Acronym** & **Meaning** \\ ANN & Artificial Neural Network \\ CLI & Centralized Learning and Inference \\ CNN & Convolutional Neural Network \\ CWS & Continuous Wind Speed \\ DA & Draft Aft \\ DF & Draft Forward \\ DNN & Deep Neural Network \\ DRL & Deep Reinforcement Learning \\ DWS & Discretized Wind Speed \\ FL & Federated Learning \\ GD & Gradient Descent \\ LLCI & Local Learning and Collaborative Inference \\ LLMS & Local Learning and Model Sharing \\ MAE & Mean Absolute Error \\ MAPE & Mean Absolute Percentage Error \\ MEP & Main Engine Power \\ ML & Machine Learning \\ MLP & Multi-Layer Perceptron \\ MSE & Mean Squared Error \\ PEP & Primary Engine Power \\ RFE & Recursive Feature Elimination \\ SGD & Stochastic Gradient Descent \\ SL & Supervised Learning \\ SOG & Speed Over Ground \\ STW & Speed Through Water \\ SMT & Ship Wind Turbine \\ UAV & Unmanned Aerial Vehicle \\ UL & Unsupervised Learning \\ URLLC & Ultra-Reliable Low Latency Communications \\ WD & Wind Direction \\ \end{tabular}