Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases-Reference-Cited by-同舟云学术

Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases

Published:2024 Issue: Volume:12 Page:6437-6454
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Angelopoulos Angelos¹^ORCID,Giannopoulos Anastasios²,Nomikos Nikolaos²^ORCID,Kalafatelis Alexandros²^ORCID,Hatziefremidis Antonios³^ORCID,Trakadas Panagiotis²^ORCID

Affiliation:

1. General Department, National and Kapodistrian University of Athens, Euboea, Greece

2. Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, Greece

3. Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, Euboea, Greece

Funder

ICOS project (“Towards a functional continuum operating system”) funded from the European Union’s HORIZON Research and Innovation Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10382497.pdf?arnumber=10382497

Reference37 articles.

1. Maritime Internet of Things: Challenges and Solutions

2. Coordination and Optimization Control Framework for Vessels Platooning in Inland Waterborne Transportation System

3. Ship Path Optimization That Accounts for Geographical Traffic Characteristics to Increase Maritime Port Safety

4. A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

5. Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

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