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Federated Learning for Maritime Environments:Use Cases, Experimental Results, and Open Issues
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  • Anastasios Giannopoulos ,
  • Panagiotis Gkonis ,
  • Petros Bithas ,
  • Nikolaos Nomikos ,
  • George Ntroulias ,
  • Panagiotis Trakadas
Anastasios Giannopoulos
Department of Ports Management and Shipping

Corresponding Author:[email protected]

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Panagiotis Gkonis
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Petros Bithas
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Nikolaos Nomikos
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George Ntroulias
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Panagiotis Trakadas
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Maritime transportation is vital for economic growth, since it is responsible for the vast majority of global trade. However, optimizing maritime transportation, focusing on certain performance metrics may lead to non-convex problems due to the large number and heterogeneity of network nodes and vessels. Furthermore, the harsh propagation environment, and the long propagation distances might be prohibitive for the implementation of conventional optimization. Machine learning (ML) represents a viable way towards complexity minimization but still, it might not be feasible to fully exploit its potential, since error-free feedback channels are usually assumed while the overall centralized processing delay from numerous distributed sources might render real-time deployment infeasible, due to stringent latency requirements. Meanwhile, security and privacy concerns constitute key driving factors for decentralized ML solutions, since data locality is vital to protect sensitive information. Taking into consideration all the above, this paper discusses feasibility issues, regarding the deployment of federated learning (FL) solutions in maritime environments, via the presentation and analysis of various use cases. Moreover, experimental results using datasets from an enterprise specializing in the maritime industry are provided, showing the superiority of FL over traditional ML approaches, in terms of accuracy and complexity. Finally, open issues that must be addressed to pave the way for the wide adoption of FL in maritime applications are discussed.