On Vessel Location Forecasting and the Effect of Federated Learning

TL;DR

This paper introduces vessel location forecasting using LSTM neural networks, comparing centralized and federated learning approaches to address privacy concerns and improve maritime traffic prediction accuracy.

Contribution

It proposes Nautilus and FedNautilus models for centralized and federated vessel location forecasting, highlighting their advantages and disadvantages.

Findings

Centralized LSTM approach outperforms current state-of-the-art methods.

Federated learning offers privacy benefits but has some accuracy trade-offs.

The paper discusses the pros and cons of federated versus centralized models.

Abstract

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.