  The wide spread of Automatic Identification System (AIS) has motivated
several maritime analytics operations. Vessel Location Forecasting (VLF) is one
of the most critical operations for maritime awareness. However, accurate VLF
is a challenging problem due to the complexity and dynamic nature of maritime
traffic conditions. Furthermore, as privacy concerns and restrictions have
grown, training data has become increasingly fragmented, resulting in dispersed
databases of several isolated data silos among different organizations, which
in turn decreases the quality of learning models. In this paper, we propose an
efficient VLF solution based on LSTM neural networks, in two variants, namely
Nautilus and FedNautilus for the centralized and the federated learning
approach, respectively. We also demonstrate the superiority of the centralized
approach with respect to current state of the art and discuss the advantages
and disadvantages of the federated against the centralized approach.
