Federated Learning for Maritime Environments:Use Cases, Experimental
Results, and Open Issues
Abstract
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.