Federated learning for green shipping optimization and management

Haoqing Wang, Ran Yan, Man Ho Au, Shuaian Wang, Yong Jimmy Jin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Many shipping companies are unwilling to share their raw data because of data privacy concerns. However, certain problems in the maritime industry become much more solvable or manageable if data are shared—for instance, the problem of reducing ship fuel consumption and thus emissions. In this study, we develop a two-stage method based on federated learning (FL) and optimization techniques to predict ship fuel consumption and optimize ship sailing speed. Because FL only requires parameters rather than raw data to be shared during model training, it can achieve both information sharing and data privacy protection. Our experiments show that FL develops a more accurate ship fuel consumption prediction model in the first stage and thus helps obtain the optimal ship sailing speed setting in the second stage. The proposed two-stage method can reduce ship fuel consumption by 2.5%–7.5% compared to models using the initial individual data. Moreover, our proposed FL framework protects the data privacy of shipping companies while facilitating the sharing of information among shipping companies.

Original languageEnglish
Article number101994
JournalAdvanced Engineering Informatics
Publication statusPublished - Apr 2023


  • Federated learning
  • Machine learning
  • Maritime transport
  • Sailing speed optimization
  • Ship fuel consumption

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence


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