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Federated learning for Spanish ports as an aid to digitization

González Cancelas Nicoleta, Molina Serrano Beatriz, Soler Flores Francisco

Journal of KONBiN

2021

Vol. 51, iss. 2

1--17

The Spanish Port System is immersed in the process of digital transformation towards the concept of Ports 4.0. This entails new regulatory and connectivity requirements, making it necessary to implement the new technologies offered by the market towards digitalization. The digitalization of the individual processes in a first step helps the exchange of digital information between the members of the port community. The next step will mean that the information flow between the participants of a port community is done in a reliable, efficient, paperless way, and thanks to technologies. However, for the Spanish port sector, data exchange has a competitive disadvantage. That is why Federated Learning is proposed. This approach allows several organizations in the port sector to collaborate in the development of models, but without the need to directly share sensitive port data among themselves. Instead of gathering data on a single server, the data remains locked on your server, and the algorithms and predictive models travel between them. The goal of this approach is to benefit from a large set of data, which contributes to increased Machine Learning performance while respecting data ownership and privacy. Through an Inter-institution or "Crosssilo FL" model, different institutions contribute to the training with their local datasets in which different companies collaborate in training a learning machine for the discovery of patterns in private datasets of high sensitivity and high content. This environment is characterized by a smaller number of participants than the mobile case, with typically better bandwidth and less intermittency.

Reducing pollution levels generated by short sea shipping : use of Bayesian networks to analyse the utilization of liquefied natural gas as an alternative fuel

Molina Serrano Beatriz, González Cancelas Nicoleta, Soler Flores Francisco

Journal of KONES

2019

Vol. 26, No. 1

147--158

Pollution adjacent to the continent's shores has increased in the last decades, so it has been necessary to establish an energy policy to improve environmental conditions. One of the proposed solution was the search of alternative fuels to the commonly used in Short Sea Shipping to reduce pollution levels in Europe. Studies and researches show that liquefied natural gas could meet the European Union environmental requirements. Even environmental benefits are important; currently there is not significant number of vessels using it as fuel. Moreover, main target of this article is exposing result of a research in which a methodology to establish the most relevant variables in the decision to implement liquefied natural gas in Short Sea Shipping has been development using data mining. A Bayesian network was constructed because this kind of network allows to get graphically the relationships between variables and to determine posteriori values that quantify their contributions to decision-making. Bayesian model has been done using data from some European countries (European Union, Norway and Iceland) and database was generated by 35 variables classified in 5 categories. Main obtained conclusion in this analysis is that variables of transport and international trade and economy and finance are the most relevant in the decision-making process when implementing liquefied natural gas. Even more, it can be stablish that capacity of liquefied natural gas regasification terminals under construction and modal distribution of water cargo transportation continental as the most decisive variables because they are the root nodes in the obtained network.