# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 1 Introduction  
Maritime transportation, an indispensable part of the world economy, relies on various associated functionalities that are related to fleet management, fuel consumption minimization, effective routing, logistics, etc. In this context, Internet of Things (IoT)-based maritime services leverage the huge amount of collected, stored, and processed data [1]. Hence, real-time monitoring and automated optimization procedures are expected to play a key role in future maritime transportation systems for ship path optimization or energy-consumption-dependent and speed optimization purposes [2, 3, 4]. To this end, traditional optimization approaches might be inefficient, as they might lead to non-convex problems, which are solved via iterative optimization approaches. Machine learning (ML) algorithms have recently emerged as a promising solution for large-scale dataset manipulation and process optimization. In the vast majority of related applications, neural networks (NNs) are properly trained in order to have an accurate representation of the input variables to the output vectors. However, if the output variables have distinct values, a clustering strategy might be more appropriate, offering significantly reduced training time compared to NNs. In general, ML training is made feasible via supervised learning (SL), unsupervised learning (UL), and deep reinforcement learning (DRL) [5]. In SL, data associations are known a priori to the system, and the algorithm is trained with a large amount of training data. In UL, there is no pattern for the associationof input variables with output metrics. As in the SL case, having a large dataset as an input prompts UL algorithms to detect any association pattern among the inputs and outputs of the system. Finally, in DRL, a mobile agent interacts with the wireless environment and tries to define the optimum set of polices via rewards or penalties associated with specific actions. After a sufficient number of training samples, it is possible to derive the optimum set of actions for all potential states. In the maritime sector, however, proper ML training involves data collection and processing from an increased number of distributed sources that cover a large geographical area with diverse propagation conditions. Hence, centralized ML approaches might not be effective, especially for highly demanding ultra-reliable low-latency communications (URLLC) services due to the large propagation distances required for efficient data gathering. In the same context, proper data manipulation is required to mitigate the effects of diverse propagation conditions. Finally, in many cases, training data might contain sensitive information for a particular maritime component, making it preferable to be stored locally and not transmitted to centralized premises [6]. In this context, and in an effort to deal with the aforementioned issues, the concept of federated learning (FL) has been proposed as an alternate ML approach that can speed up ML execution times while keeping data localized [7; 8]. In FL, maritime nodes exploit shared models trained from excessive amounts of data without the need to centrally store them. Considering the above, this paper aims to explore the diverse applications of federated learning within maritime environments. It underscores the necessity of implementing FL in the maritime sector, highlighting its advantages in predicting critical parameters and identifying faults while ensuring security and data privacy. To demonstrate the significance of FL in maritime transportation systems, this paper includes an experimental study on fuel consumption prediction, utilizing real maritime industry datasets. The findings reveal that FL enhances prediction accuracy without increasing complexity compared to other distributed and centralized learning methods. As FL is an emerging field, this paper also addresses various open issues and research directions, aiming to foster further interest and development in the context of 6G-enabled maritime services.