Efficient privacy-preserving federated learning method for Internet of Ships

Abstract

ObjectivesArtificial intelligent technologies have become an important approach to improving the safety of shipping and reducing the operating costs of shipping companies. In order to further improve the level of ship intelligence and break down the data barriers between different shipping companies, an efficient privacy-preserving federated learning method (EPFL) is proposed in this paper.MethodsFederated learning is adopted to organize multiple ship participants to collaboratively train a global fault diagnosis model, and cryptography technologies are used to protect their local data information. Considering Internet of Ships (IoS) scenarios, this paper introduces sparsification technology to compress the model parameters uploaded by shipping participants and reduce their number.ResultsTheoretical analysis and the experimental results show that the proposed EPFL method can effectively reduce the resource consumption of cryptographic computation and data communication while protecting the local data information of ship participants.ConclusionsThe proposed EPFL method can provide references for the establishment of intelligent ship systems.

Keywords

• internet of ships
• deep learning
• federated learning
• privacy-preserving
• sparsification